CHAMMI-75: Pre-training multi-channel models with heterogeneous microscopy images
Authors: Vidit Agrawal
John Peters
Tyler N. Thompson
Mohammad Vali Sanian
Chau Pham
Nikita Moshkov
Arshad Kazi
Aditya Pillai
Jack Freeman
Byunguk Kang
Samouil L. Farhi
Ernest Fraenkel
Ron M. Stewart
Lassi Paavolainen
Bryan A. Plummer
Juan C. Caicedo
Venue: ICLR 2026 Poster (2026)
Keywords: microscopy
representation learning
multi-channel imaging
self-supervised learning
biology
Primary area: datasets and benchmarks
TL;DR: Dataset for pre-training multi-channel imaging models in microscopy and benchmarks in cellular biology applications

Abstract: Quantifying cell morphology using images and machine learning has proven to be a powerful tool to study the response of cells to treatments. However, models used to quantify cellular morphology are typically trained with a single microscopy imaging type. This results in specialized models that cannot be reused across biological studies because the technical specifications do not match (e.g., different number of channels). Here, we present CHAMMI-75, an open access dataset of heterogeneous, multi-channel microscopy images from 75 diverse biological studies. We curated this resource from publicly available sources to investigate cellular morphology models that are
channel-adaptive and can process any microscopy image type. Our experiments show that training with CHAMMI-75 can improve performance in multi-channel bioimaging tasks primarily because of its high diversity in microscopy modalities. This work paves the way to create the next generation of cellular morphology models for biological studies.

Published as a conference paper at ICLR 2026
CHAMMI-75: PRE-TRAINING MULTI-CHANNEL MOD- ELS WITH HETEROGENEOUS MICROSCOPY IMAGES
Vidit Agrawal 1:2 John Peters! Tyler N. Thompson 1.2 Mohammad Vali Sanian 3.4 Chau Pham Nikita Moshkov Arshad Kazit.2 Aditya Pillai Jack Freemant, Byunguk Kang?., 7,8 Samouil L. Farhis, Ernest Fraeukel Ron Stewart Lassi Paavolainen Bryan A. Plummer?, and Juan C. Caicedo
Morgridge Institute for Research, Madison, WI, USA
2 University of Visconsin-Madison, Madison, WI, USA
Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland UUniversity of Helsinki, Hclsinki, Finland
Boston University, Boston, MA, USA
Institute Computational Biology, Helmholtz Munich, Neuherberg, Germany "Msssachusetts Institute of Technology, Cambridge, MA, USA $Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Figure 1: CHAMMI-75; heterogenous dataset of multi-channel microscopy images. Pseudo-color images representing 75 sources, and edge colors indicating their original number of channels.
ABSTRACT
Quantifying cell morphology using images and machine learning is power- ful stratogy to study the response of cells to treatments. However, models used quantify cellular morphology are typically trainod with single mi- croscopy imaging type. This results in specialized models that cannot be reused across biological studies because the technical specifications do not match (e.g., different number of channels). Here, we present CHAMMI-75. an open access dataset heterogeneous, multi-chanmn microscopy images from 75 diverse biological studies. We curated this resource from pub- licly available sources to investigate cellular morphology models that are channeladaaptie and process any microscopy image type. Our periments show that training with CHAMMI-75 can improve performance in multi-channel bioimaging tasks primarily because of its high diversity in mi- croscopy modalities. This work paves the way to create the next generation of cellular morphology models for biological studies.
INTRODUCTION
Microscopy is a versatile scientific tool in experimental biology and allows rescarchers to acquire images of cells under controlled experimental conditions. Unlike natural images, which are consistently acquired and stored in a three-channel, RGB format, microscopy images have varied number of channels; anywhere from one to dozens, each encoding

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Multi-channel
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Figure 2: Comparison of existing microscopy datasets used for representation learning of cell morphology. CHAMMI-75 is the largest dataset of multi-channel microscopy images. Others datasets:
IDRCel1100k (Bourriez et al.]
2024
CHAMMI (Chen al. 2023). CytolmageNeet (Hua et al.
2021), HPAv23 [Gupta et al.
2021).
Microsnoop (Xun et al. 2024), RxRx (eexxxeecssoon (002 JUMP-
CP
Chandrasekaran et al.
2023a
and Phenoprints- 16M
Kenyon-Dee Deoon et al. 20224).
different type of signal. Deep learning is widely adopted analyze microscopy images
(Moen et al. 2019 Volpe et al. 2023 Pratapa et al. 2021 Xing et al. (017), but the most common strategy to create such models is to modify architectures developed for RGB images by changing and fixing the number of channels according to the problem (Doron et al. 2023
Gupta et al. 2024). This limits the ability to reuse models from experiment to experiment, or to pre-train large-scale models that accumulate universal knowledge of cellular biology.
Multi-channel imaging models have emerged to address the limilations existing vision ar- chitectures, enabling the processing of varied number of channels at test time (Kraus et al. 2024 Bourriez et al.) 2024 Pham & Plummer 2024 Pham et al. 2025 common trend in such architectures is the separation of channels individual modalities, resulting in flexible, variable-length inputs with diverse channel types. While these initiatives represent an im- portant step towards creating foundation models for cellular imaging, most of them are still small-scale, proof-of-concept experiments. The main reason is the lack of standardized and well-curated datasets to properly test these ideas large scale. Multi-channel microscopy images are publicly available, but it is not straightforward to put them together in a single, useful resource for machine learning research because of their technical differences, varied formats, and inconsistent metadata.
We present a new multi-channel microscopy image dataset that we call CHAMMI-75 (Figure) D Our dataset is larger than prior work (Figure and combines diverse heterogencous sources of biological images into a single resource to investigate cellular morphology. CHAMMI-75 con- tains more technical and biological variation than typically used to train cellular imaging models (Figure 3). For example, CHAMMI-75 includes images of organisms and 223 cell lines collected with different microscopes, at different resolutions, and with different num- bers of channels. Our goal was to obtain a representative visual sample the cellular biology universe that can be observed with microscopy. With the accelerated progress of machine learning, this diversity comes as a valuable source of information to train the next generation of multi-channel imaging models. Ultimately, we believe that foundation models for microscopy imaging should be able to understand cell morphology at all scales, in all cell types, and independently of the imaging technology used for observation.
In this work, we address the data gap that hinders the development of generalizable mod- els for microscopy imaging. Progress in machine learning is frequently possible thanks to rigorous, large-scale curation efforts to create open datasets. For example, Image (Deng) et al 2009) and LATON (Schuhmann et al. 2022) fundamentally shifted the focus of repre- sentation learning rescarch and supported breakthroughs that would have been impossible without such data. In computational biology, challenging and realistic data is similarly necessary to advance the field. While significant progress has been made with fixed-channel image models, many open problems still exist to achieve a general understanding of cellular states regardless of the imaging technology. CHAMMI-75 is the first resource to integrate heterogeneous multi-channed imaging data at this scale in a way that directly facilitates the investigation of these challenges.
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Figure 3: Diversity sources and biological studies CHAMMI-75. The treemap illustrates distribution of images according to the hosting platforms they were obtained from (colors) and the type biological study (inner rectangles). We sampled from 18 different sources to ensure broad coverage of biological study types.
The purpose of CHAMMI-75 is to facilitate the creation of models for cell phenotyping, which the task of quantifying morphological differences between cellular states (e-g., healthy perturbed cells). This goal is different from cell iogmentation, a critical task in bioimage analysis that has seen the emergence of robust foundation models (Stringer
al. 2021
Pachitariu & Stringer 2022; Pachitariu et al.) 2025 Archit et al., 2025 Marks et al. 2025 These models demonstrate success in generalized prediction of cell masks across modalities, which is often a required step before cell phenotyping. CHAMMI-75 provides the data nec- essary train and evaluate models that can detoct subtle biological classification signals, not available through segmentation alone.
The contributions of our work are: (1) large, heterogeneous dataset of multi-channel microscopy images, with 25 channel types and high technical and biological variation. (2) Three new datasets and benchmarks to evaluate multi-channel model performance, which represent real-world contemporary biological studies with novel channel combinations (e.g., 14-channel images). (3) A systematic experimental evaluation to investigate the usefulness of dataset pre-training resource, using self-supervised learning. The results show that the diversity CHAMMI-75 a strength for yiclding models that perform well a varicty of challenging biological lasks. (4) A top performing model pre-trained with CHAMMI-75, which we call MorphEm. (5) The data, code, and models are publicly available for future http:/Caaicooaab/CCHMM-----
research and development in the field: nttts://gitthe
RELATED WORK
Figure provides high-level overview comparison of our dataset and prior work. The biological imaging community has a long tradition of publicly sharing the datasots obtained in their studies. Prominent initiatives to store and share imaging datascts include the Image Data Repository (IDR) (Williams et al. 2017; the Bioimage Archive (Hartley et al.) 2022 and the Cell Painting Gallery WWissbart et al. 2024). Other major projects create large datasets of cells exposed to many meets to serve as a map to investigale various biological questions. These include the RxRx datasets (Sypetkowski with the JUMP-CP dataset (Chandrasekaran et al.) 2023b/, which cover large number of genetic and chemical perturbations. Similarly, the Human Protein Atlas (Thul al. 2017). the OpenCell project (Cho et al.) 2022). and Allen Institute for Cell Science Viana et al. 2023) have created image datasets to map the localization of proteins in human cells. While all these resources have multi-channel images and are publicly available, they are typically used and analyzed separately. Closest to ours IDRCell100k (Bourriez et al. 2024), dataset created for multi-channel model development containing 100K images from 79 different sources. Inspired by their work, we extend the effort by collecting 30x more images with superior data curation, quality control, and metadata annotations.
al. [2023). along
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Figure 4: Content and distribution of images in CHAMMI-75 according to the integrated metadata. a) Selected metadata fields and summary statistics of their diversity. b) Sparse, long-tail distribution of channel configurations across studies. None of the studies has all channel types, and none of the channels is used in all studies.
Prior work has explored channcl-adaptive imaging (c.g., (Bao 2024 Kraus et al.,
2023 Bourriez al., Pham & Plummer 2024 Pham et al.) 2025 and where the number and configuration of the imput channels is not lixed, they either train on private small, publicly available datasets 2023 Other work either focuses on weakly-
2024
data (Kraus et al.
2024 Kenyon-Dean et al.] 2024).
for proof-ol-concepl experiments (Chen et al.
or self-supervised methods over fixed channels Caicedo et al. 2018 Moshkov et al. 2024
Doron et al.
2023 Kim et al. 2025), or developing channel- Shannel-agnostic methods (Pawlows
ct all 2016 Ando et al. 2017 7 Xun et al., 2024 Morelli ct al.
2025 Lian ct al. 2025
De Lorenci et al. 2025). These studics provided insight into how image- based profiling can
reveal the response cells to biological reagents (Caicedo et al.
2017 2016), and it can be scaled to high-throughput experiments using robolic autoraatio (Boutros el al. 2015). As our dataset contains varying numbers of channels, our experiments use representatives of both channel-agnostie and channel-adapting modeling.
3 THE CHAMMI-77 DATASET
CHAMMI-75 is collection of 2,792,462 fields-of-veew (FoV) for pre-training sampled from
74 publicly available microscopy imaging studies (Table A.1]. ), and 2,474,875 FoV held out for test and organized in six benchmarke (Table A.1. Each FoV in the pre-training set is a multi-channel microscopy image with up to seven channels, and may have a single or multiple cells (Figure D). Our dataset is the largest multi-channel image datasct for model pre-training in microscopy. Compared to existing microscopy imaging datasets, CHAMMI- 75 draws from many more sources and contains many more channel types (Figure 2).
The process of building CHAMMI-75 followed three major phases. We started with a data acquisition phase for selecting hosting platforms, identifying source datasets, downloading raw data, and standardizing image formats. Next, we conducted a metadata integration phase where all the experimental details of the downloaded images were collected, organized, and integrated across all sources. The final phase was data curation, which focused on strategically sampling the most representative and informative images for learning. This phase transformed the downloaded image set collected in the first phase into CHAMMI-75.
Data acquisition. The CHAMMI-75 datasel was collected from different hosting plat- forms that store biological images from published scientific studies (Figure 3). The top platforms of imaging data according to the number of studies are: The Image Data Re- source (IDR) (Williams et al. 2017). Zenodo (CERN & OpenAIRE 2013), Mendeley Data Elseevir 2025), and Figshare Singh 2011 The full list of sources is provided in Appendix A.1 These repositorics provide access to image sets created by biological labs around the world with open data licenses; 97% of datascts in our collection have a Creative Commons
license (60% CC BY 4.0, 12% CCO 1.0). The 75 studies are listed in Table A5
From these hosting platforms, we selected 75 source datasets with highly heterogeneous biological and technical settings. Biological diversity
defined in terms of organisms and types of perturbations, while technical diversity was defined in terms of microscopy techniques and imaging settings (Figure More details in Appendix A.2
Four of the six benchmarks share images with the pre-training set. Total of unique sources: 76.
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Figure 5:
Dataset curation pipeline. Left: the dataset downloaded from the hosting platforms. Middle: the metadata is used to filter redundancy by randomly sampling few: 2D slices from 3D images, frames from live microscopy videos, and wells from control conditions. Right: content- based clustering selects diverse, high-quality images final step to create CHAMMI-75.
Metadata integration. We collected metadata information to prepare consistent infor- mation for guiding the sampling of high-quality, diverse images for the pre-training set. Not all images are equally informative or uscful, and we targeted biological and technical vari- ables reported in the original studies for filtering purposes. As a result, the metadata table has 22 columns organized in 6 groups depending on the type of information (Figures 4 and A1l.. These values were obtained from the original sources by parsing information from (I) available resource descriptor files, (2) values encoded in image filenames, and (3) details reported in the source paper. Most studies have scientific publication that describes ex- perimental details, and for part of the metadata preparation, we used large-language models to assist with the identification and organization of certain information. The final meta- data was parsed programmatically using deterministic rules and then manually curated and validated. (Appendix A.3
Data curation. Data curation is an important elfort to successfully scale represental iin learning (Vo et al. 2024 Siméoni et al. 2025)- Our goal was select. diverse sample that represents the main factors of variation in the ~22 downloaded images. To this end, we systematically sampled images following information in the metadata table. Figure illus- trates the main steps of the dataset curation, designed to minimize redundancy and select informative images from all sources. The main filtering steps include 3D stack sampling, temporal sampling, control-basod sampling, and K-mcans based filtering (Appendix A.4). By carefully curating images this way, we selected 2.8M diverse multi-channel images from multiple sources, resulting in the pre-training dataset CHAMMI-75.
Content annotations. We analyzed image contents to produce annotations about the locations of single cells. Specifically, we configured cell segmentation pipelines for all sources using Cellpose (Stringer et al. 2021 Pachitariu & Stringer, 2022) primarily to detect the mucleus (when available) for 1.8B single cells found in the 2.8M multi-channel images. This information is useful to generate crops that contain visible cells during training, thus avoiding empty, noisy, or non-informative regions. More details are reported in the Appendix A.5
the cell body. We recorded the center of mass coordinates
EVALUATION BENCHMARKS
Here we briefly describe six datasets and associated evaluation tasks that we adopt for performance evaluation, which represent real-world, contemporary imagee-ased biological sludies. Details about the benchmarks are reported in Figure 6 and in Appendix A.6
CHAMMI benchmark. This benchmark (Chen et al. 2023 contains about 220K multi- channel images in three subsets with different numbers and types of channels, and includes six out-of-domain generalization tasks. The tasks include: cell-cycle stage classification (3 channels, WTC-11 dataset), protein localization classification (4 channcls, HPA datasct), and replicate treatment retrieval (5 channels, LINCS Coll Painting datasct). We follow the standard evaluation protocol and report the CHAMMI score.
IDR-0017. A chemical-gen -henicc--genetic interaction study (Brinngg et al. 2015) that includes 122,880 two-channel images of 12 cell-lines treated with 2220 compounds. The task is to identify
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IDR0017
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Nucleus, Cytosivelarton
Hit detection
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1280 compounds, cal lines
Nucieus, ER, Mitochondria, Protein
Protein localization
Casstiiation
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JUMP-CPI Compouunds
Nucieus, RNA, ER, Golgi, Mitcchonrii
Phenotypic Activity constoenry 306 chemicas, 758 tirgets
Kean Average Prectioon
1200 tatel 0% pre-tratming
CellPHE Cheenel generaizattion
Phenstyype
Binayy
RBC MC: Cross-done domann gencralizaton
Bright fold
Nullkrbel lebeel
Morphology classification Two cinical domanss
Accuracy
0% in pre tralning
130, sctal
We adopted existing benchmarks (HPAv23 (Gupta et al. 2024) and JUMP-CP1 (Chandrasekaran et al., 2023ab), and introduce three new from real biological studies (IDR0017 (Breinig al.j 2015). CellPHIE (Kang al. 2025), and RBC-MC (Doan et al. 2020 for channel and cross-domain generalization.
Figure 6: Illustration of the evaluation in CHAMMI-75.
hits among the combinatorial experiments by ranking gene-compound combinations that are likely to have a large effect with respect to controls. Ground truth hits were obtained from the original study, and performance is evaluated using AUROC as well as recall the top 50 and 100 items of the ranked list.
HPAv23 at 256 256. НPA studies the localization of proteins in human cells by classifying images into the correct localization category (Ouyang et al., 2019 Le et al. 2022). We adopt the dataset HPAv23 with 1.1 million images of single cells and annotations for protein localization classification in 19 or 31 classes. The original image size is 1024x 1024, and we reduced to 256x256 to accelerate benchmarking (Apendix G.4.2). We preserve the rest of the benchmark intact, and report average precision scores accordingly.
JUMP-CPI Compounds. We adopt a subset of the JUMP-CP (Chandrasckaran al. 2023b dataset, which includes 24K five-channel images of U2OS cells perturbed with 306 chemicals at two time points. There are two ranking tasks in this benchmark: 1) phenotypic activity: identify compounds with response significantly different from negative controls, and 2) phenotypic consistency: measure whether groups biologically related compounds average precision for both tasks.
clustered together. We report
CellPHTE. A pooled genetic perturbation screen to investigate Huntington's Disease (Kng ct all 2025). this dataset contains 57K 14-channel images of single cells perturbed with one of 19 genes. A non-targetting control is used as reference to determine the effect of perturbations. The task is to classify single cells in a binary classification setting: non- targetting vs perturbed gene. Images from this dataset are not included in the pretraining dataset, resulting in novel channel combination benchmark.
Red Blood Cell Morphology Classification (RBC-MC). A set of 130,560 single- channel bright-field images (48x48 pixels) of RBCs obtained via imaging flow cytometry from two distinct clinical sites (Swiss and Canadian blood banks) (Doan et al. 2020 The task is multi-class classification into seven climically relevant morphological categorics associated with blood quality. The benchmark employs cross-domain validation protocol with a linear probe trained on data from one site and tested on the other (Swiss vs Canadian).
EXPERIMENTS AND RESULTS
We identify two main multi-channel strategies that are emerging in recent work: (1) Bag of channels (BoC) models, which train backbone networks that read one channel at a time and then concatenate features for downstream tasks. Examples include Microsnoop Xun et all, 2024), uniDINO (Morelli et al., 2025), and DINO-BoC (De Lorenci et al. 2025 (2) Multi-channel attention (MCA) models, which unravel channel tokens into a single. long sequence to model cross-channel associations. These include Channel-ViT (Bao al.

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Table 1: Performance of models across benchmarks. Column legend: meT: Multi-channel train- ing, meM: Multi-channel mechanism ( manual selection of channel combination, bag of channels, fn variable-length sequence of channel tokens); Dataset: Pre-training dataset (áá Mi- croscopy Images, 100 Natural Images), CM: CHAMMI, H: HPAw23 256x256, J1: JUMP-CP1, J2: JUMP-CP2, I: IDR0017, CP: CellPHEE (channel generalization) R: RBC-MC (cross-domain generalization). All models are ViT-small and have been trained with SSL, except for the lop-line results of SubCell (gray row), which is collection of specialized, larger models (ViT-base) trained with multi-objective, weakly supervised learning. The result of best performing SubCell model for each benchmark presented. Bold numbers are best result among SSL methods.
Model Characteristics
Benchmarks
Model
meT meM Dataset
CM
H
J1
J2
I
CP
R
SubCell
HPAv23
53.98
69.33
77.60
07.44
75.37
71.23
59.10
DINOv2
X
L42M
37.93
53.76
75.84
07.03
75.22
72.27
59.41
OpenPhenom
V
RxRx + JUMP
38.22
49.13
74.26
04.99
75.19
75.56
64.43
IDRCell
V
IDRCel1100k
37.38
44.05
72.37
04.97
75.19
79.14
55.85
MorphEm (oum)
CHAMMI-75
48.75
58.87
76.32
06.79
75.52
80.51
68.34
2023 CA-MAE Kraus et al. 2024). and ChA- Phaa et al. 2025 among others
Phha & Plummer 2024; Bourriez et al. 2024 Here, we benchmark VIT models with the BoC approach, and analyze their scalability properties together with Channel-Vii representative architecture of MCA models.
5.1 BENCHMARKING EXPERIMENTS
We trained a ViT-small model with DINO-BoC (De Lorenci et al. 2025 on CHAMMI-75 and compare the results against cxisting models used to obtain representations of cellular images. Performance is measured in the six benchmarks described in Section A
Baselines. We consider state-of-the-arl pre-trained models that. have been recently released for cellular image analysis. We start with SubCell (Gupla el al. 2024), suit of ViT-base models trained with the HPAv23 dataset using multi-objective, weakly supervised learning. SubCell has four fixed-channel models (one 2ch, two 3ch, one 4ch) trained in two modes (MAF-Cells, ViT-ProtS). These models exhibit excellent performance in downstrcam tasks; however, manual configuration is needed to decide channel combination and model type. The variation of results between SubCell models is substantial (Appendix F.I.1). making its usage challenging and computationally expensive to test in practice. We also evaluate OpenPhenom (Kraus et al., 2024), a channel adaptive ViT-small model trained on five and six-channel Cell Painting images, and DINOv2 (Oquab et al. 2023). which is fixed RGB channel model adapted for multi-chan channel images using BoC. Finally, we trained a model with IDRCel1100K (Bourriez et al.] 2024). a multi-channel microscopy image dataset close to ours in number of sources (79 vs 75) but smaller (100k multi-channel images).
Results. Table shows pro-training channel-adaptive architectures with SSL yields models that are generally useful in many tasks, regardless of the number of channels. SubCell sets top-line results across several benchmarks; its strong performance may be explained by factors such as training with biological objectives, larger ViT models, channel specialization, and manual selection of best results across their different settings. Our BoC model which we call MorphEm (for Morphology Embeddings)- trained with CHAMMI-75 obtained the best performance in six out of seven performance metrics, demonstrating goneralization in tasks with varying channel configurations. The same model trained with IDRCell 00k (a multi-channel microscopy image dataset) underperforms in most tasks, suggesting that CHAMMI-75 contains additional informative images and higher quality data for learning. OpenPhenom also underperforms in several tasks, and while it is channel adaptive, it was trained exclusively with Cell Painting data (RxRx & JUMP-CP), Overall, our model exhibits strong performance in challenging tasks thanks to a combination of simple methods and high-quality, well-curated data.
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5.2 CHANNEL GENERALIZATION AND CROSS MODALITY TRANSFER
We explore channel gencralization through the most challenging and realistic scenarios en- countered in biological practice. First, generalization to novel channel combinations, which is very frequent in laboratories by combining known channels in novel ways. The CellPHIE (CP) benchmark, with its unique 14-channel configuration, serves as a real-world test for this capability. Second, generalization to novel modalities and domains. The RBC-MC (R) benchmark tests this by using single-channel bright-field imaging flow cytomotry (modal- ity) to classify red blood cell morphologies. Importantly, its paired cross-domain evaluation across clinical sites, challenges models encode biologically relevant fcatures.
The results are summarized in the last two columns of Table I and indicate that our MorphEm model trained with CHAMMI-75 yields the best performance both generalization tasks. This performance is in contrast with highly specialized models like SubCell, which lags behind in these novel conditions. Specifically, our smaller, SSL-trained model outperforms the larger, WSL-trained SubCell by 13% in CellPHIE and 15% in RBC-MC. This result suggests that the scale and diversity of CHAMMI-75 are important factors for achieving robust. channel and domain generalization.
5.3 FACTORS IMPACTING REPRESENTATION LEARNING
To understand which factors of variation in CHAMMI-75 drive ro- bust representation learning, we performed six targeted data abla- lion studies, systematically sepa- rating training images according to variables in the metadata. In each ablation, we train a DINO- BoC model each subset and then measure their relative differ- in CHAMMI scores (Figure 7). The results reveal hierar- chy of factors that influence model performance. First, specialized vs. heterogeneous data: models trained only with the target data under-perform by 27% relative dif- ference, while models trained with CHAMMI-75 can improve perfor- mance up to 38%. This indicates that heterogencous data facilitates learning representations that more expressive and are less prone to overfitting.
40.02%
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CHIAWW score WTC soore
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relk attrren
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charmeii
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Magnittction Cell line
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Figure 7: Data ablation study with six factors of variation. Heterogeneity and microscopy modality major drivers of performance improvement in CHAMKI-75 (Appendix B
The second most influential factor is the diversity of imaging modality (microscopy type). A model trained only on the two dominant modalities (fluorescence and epi-fluorescene) un- derperforms by 13% relative to model trained on the other 12 less-represented modalities, which improves performance by 15% relative. The next most impactful factor is microscope magnification with relative performance difference of ~ 3%. These results indicate that broad exposure to technical variation makes model more generalizable. Other factors, such as training without the most common cell lines (U20S and A549), or limiting the number of channels (c.g., training only on studies with up to two, three, or four channels), resulted in minor performance changes (~ 1%). This indicates that while highly diverse cell lines and varied channel counts contribute to overall robustness, the diversity of the physical imaging process (modality) is the main variable that improves representation learning.
5.4 SCALABILITY ANALYSIS
We evaluate CHAMMI-75 as a pre-training resource through three main evaluations. (a) Dataset scaling: assess performance on the CHAMMI benchmark (six out-of-distributioo
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Figure 8: Scaling properties of sepe-supe,, multi-channel models. (a) Dataset, (b) model, and (c) compute scaling results the CHAMMT benchmark. SFC supervised fixed-channels, BoC bag of channels, MCA multi-channel attention, * MorphEm model, scaled with best configuration.
phenotype matching tasks) by increasing the amount of multi-channel images used for train-
ing. The baseline the average three specialized models (Ids, ~33K images, 3, 4, or channels). We then combine sets: CHAMMI subsets (3ds, 100K images, 8 channels), 10 datasets (10ds, 178K images, 14 channels), and a sample of CHAMMI-75 75 (74ds-small, 560K images, 25 channels). The full CHAMMI-75 set (74ds-large, 2.8M images, 25 channels) was only used to train MorphEm for final benchmarking. (b) Model scaling: we fix the dataset size (74ds-small) and increase model parameters. In all cases, we train ViT models, BoC (single channel) and MCA (variable channels), with 224x224 input size using three SSL algorithms (SimCLR, MAE, DLNO), (c) Compute iterations: we keep dataset and model sizes fixed and evaluate the impact of using more compute to train models. Hyperparame- ters are kept consistent where possible. After SSL training, fcatures are extracted from the CHAMMI test sets with frozen weights and no finetuning. See Appendix C and G.2
Consistent with prior work, models trained with SST. benefit from more data. Comparing SSL performance against fully-supervised, specialized models (an upper bound), our re- sults (Figure bb) confirm that as data and model size increase, SSL models approach the specialized supervised performance. This shows that single, scaled, unsupervised multi- channel model can be highly competitive. The primary performance determinant the multi-channel strategy (BoC MCA), followed by the SSL algorithm and model size. BoC models outperform MCA in the SSL regime, suggesting difficulty in learning cross-channcl correlations in an unsupervised way. BoC models also easier scale, as MCA requires 3X to 5X more GPU hours due to longer sequences (Figure G23 We also observe that when keeping compute constant (total iterations per epoch, Figure gp), BoC models with access to more data can yield improved performance. Based on these findings, we trained MorphEm, BoC ViT-small model using DINO on the full CHAMMI-75 dataset (5X larger), requiring 2,352 GPU hours. This yielded 9.8% relative improvement over the best result from the datasct scaling evaluation, with potential for further gains using larger ViT in future work.
5.5 MODEL TYPE AND DISENTANGLEMENT ANALYSIS
Model design choices. The experimental results suggest the following factors impacting performance: (1) Multi-channel strategy: the BoC approach was more effective and scalable under the SSL regime, yielding up to 19% relative improvement over MCA, at fraction of the computational cost. (2) SSL method: among the tested methods, DINO was the top-performing algorithm, showing ~ 15% relative improvement over MAE and ~ 7% relative improvement over SimCLR. (Figure BA). (3) Model size: we observed consistent performance gains from model scaling. Moving from ViT-small to ViT-large resulted in relative improvement 10% (Figure 8b). (4) The use of weak supervision, even with noisy labels, improved performance by 1-19% relative to the baseline in the MCA-SSL setting. These results provide guidance for future research on multi-channel models as the field continues to bridge the gap between self-supervised and supervised performance.

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Disentanglement and batch correction. We adopted the batch correction benchmark of JUMP-CP (Arevalo et al., 2024) to evaluate the ability of models to separate technical from biological variation. The results indicate that features learned with CHAMMI-75 are more robust to technical variation (requiring less batch correction) and maximize biological signal. Following correction with the Seurat. CCA algorithm (Stuart et al. 2019), achieve high separation of biological clusters from technical batches, performing better than features extracted with CellProfiler and those learned from the IDRCe11100 dataset. A qualitative analysis of the feature space (Appendix D) using UMAP visualizations of single- channel features confirm that the primary clustering factor the source study (technical domain). This shows that models are sensitive to technical variation, and confirms the need to apply batch correction to maximize biological signal (Appendix Ej.
features
5.6 WEAKLY SUPERVISED LEARNING
The state-of-the-art in image- based profiling introduces prior knowledge of biological conditions using weakly supervised learning (WSL) with objectives such treatment or protein classification ion (Gupta et al. 2024 Moshkov al., 2024]. While these pretext tasks are not focused on the primary model use, they improve perfor- mance as long as the labels are clean and consistent enough to fa- cilitate learning. Table 2 evaluates two WSL models trained with 74ds-small using the reagent identifier uated here implement multi-channel attention because biological guidance may require a combination of channels to learn meaningful representations. The WSL method ChA- MAEVIT (Pham et al.] 2025)) obtains best performance, demonstrating the ability of CHAMMI-75 to benehit multiple learning strategies.
Table 2: Performance of WSL models ChA- MAEVIT (Pham et al. 2025 and MCA-SupC (Khosla
et all 2020) across benchmarks, with a DINO-based SSL model (MCA-SSL) included for comparison.
Benchmarks
Model
CM
H
J1
J2
I
CP
MCA-SSL
37.18
37.46
61.72
01.85
24.40
67.93
MCA-SupC
36.15
51.81
72.72
05.20
25.21
75.59
ChA-MAEVIT
38.72
56.92
74.99
05.53
24.99
76.12
target in the loss function (see Appendix A.7]. The models eval-
CONCLUSION AND LIMITATIONS
Conclusion. We introduced CHAMMI-75, datasct of heterogencous multi-channel mi- croscopy images for pre-training cellular image analysis models. The dataset combined images from 75 sources, resulting in curated, high-quality resource that has more biologi- cal and technical variation than other microscopy datasets used in prior work. Our dataset paves the way to investigate multi-channel imaging models at large scale, and can facilitate the development of models that work seamlessly across biological labs and image configu- rations. We adopted existing benchmarks and introduced new ones to continue evaluating progress in the ever-changing world of microscopy. Our experimental results show that CHAMMI-75 can be used to scale models that yield strong performance across the bench- marks. Importantly, improved generalization can be achieved when training models with heterogeneous imaging modalities, resulting in models that perform well in novel channel combination settings, novel modalities, and that learn to disentangle biological and technical variation. We make our lop-performing model, MorphEm, publicly available.
Limitations. The experimental evaluation was limited by the computational resources available in academic institutions. This work was focused on high-quality data curation and leaves the investigation of novel methods for multi-channel modeling for future re- search. The collected metadata is informative but noisy despite best efforts to standardize all sources. This represents a real-world condition of imaging data, and generates challenges for supervised methods or similar types of studies. While the presented dataset is large and representative of many microscopy imaging types and biological conditions, it is also sparse and does not cover all relevant. variables in balanced way.
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ETHICS STATEMENT
We have no ethical concerns with our work. All the datasets sampled in our 75 studies have been cited with their licenses. The licensing information can be obtained in the Appendix A5 All these datasets can be used for furthering scientific research, and understanding foundation models in microscopy but not for commercial purposes.
REPRODUCIRILITY STATEMENT
For reproducibility purposes, we will release our best model weights, dataset and the code on suitable platform upon acceptance of the paper. We hope that the community can use this work as resource for further research into foundation models for cellular microscopy.
CODE AND DATA AVAILABILITY
The code to reproduce the results of is available on GitHuub https::/githhub.com https://gghbb..co//cccooooaa Baticcoooab//HAMMMM.
The data can be downloaded from the following S3 bucket through AWS Open Data:
The best ViT-small model trained on CHAMMI-75 is available on Hugging Face:
https://huugeer hhggss//gguggggace..ooo http:/ggaaaaeeeo/aaaaaaaaaaaaaa/ /aacedool.bb/MMrrhiih
The tutorials on how to use the code and model are available at: /llb/////n//// bta////ll//mmm//////////a/aaaa// hasti//wstuutrials
https://githuu.com
ACKNOWLEDGEMENTS
The authors are grateful to Gantugs Atarsaikhan and Zayn Kayali for numerous contribu- tions to the early stages of this project. We thank the CHTC team at the University of Aarconsin-Madison for their guidance, support, and access to compute infrastructure. The authors also wish to acknowledge CSC IT Center for Science, Finland, for awarding this project access to the LUMT supercomputer, owned by the EuroHPC Joint Undertaking, hosted by CSC (Finland) and the L.UM consortium. This work was supported by the Na- tional Science Foundation under Award No 2348683 (to JCC) and 2134696 (to BAP), and by the National Library of Medicine, NIH Grant 5T15LM007359, Funding was provided by the Research Council of Finland with awards 340273, 346604, 359907 (to LP, MVS), and the University of Helsinki Doctoral Programme in Computer Science (to MVS). This work was also supported by an unrestricted grant from Meta AI, the AWS Open Data Sponsorship Program, and the BU AI Research Resource Program.
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A DATASET
The CHAMMI-75 dataset contains two versions for pre-training: a small dataset for devel- opment and ablation studies, and large datasct for scaling models. Table A3 reports the mumber images in cach set.
Table A3: Number of multi-channed images in the small and large pretraining sets
Set
# Single Channel Images
#
Multi-Channel Images
Small
1,679,766
560,558 2,849,483
Large
8, 8,029,583
A.1
DATA SOURCES
The following are the official data sources or hosting platforms where we obtained microscopy
images curate the CHAMMI-75 dataset alphabetical order: Allen Institute (Viana et al.
2023
AMSActa (of Bologna. 2025 BBBC (Ljosa et al. 2012). Biolmage Archive (Hartley
et al.
2022); BioStudies, Cel Painting Gallery (Weisbart et al. 2024], Edmond (Society
2025
EMPIAN
IAR (ludin et al. 2023), Figshare (Singh 2011p, Gitflub (Kaggle 2025), HPA
(Thul
et al.
2017p, IDR (Wiliams et al. 2017). Mendeley Data (Elsevier 2025 OSF
OSF 2025
Recursion (Recursion 2025). University of Reading Research Data Archive
of Reading 2025 ) Zenodo (CERN & OpenAIRE 2013). Each the 75 datasets are
listed in Table A5 with their corresponding licenses, number of channels per image, and the
number multi-channel images sampled from the original dataset.
Table A4: Pre-training Image Dataset Information
Dataset
License
Channels
Images
hpa0018
HPAv18
(HPA)
Thul et m1
2017
CC BY-8A 4.0
113,645
(HPA)
5d-0001
IDR0001
(1DR) Grami
112,476
5d-0002
IDR0002
(IDR) Heriche 01 m.
2111
CC BY 4.0
27, 07.092
idr0003
IDR0083
(IDR) Broket
211
CC BY-NC-8A 3.0
43,072
5de0005
(DD))
BY-NC-8A
34,698
idr0006
IDR0006
IDDI) Look 01 3m
201
BY-NC-SA 3.0
125, 508
idr0007
IDR0007
(DRR SPikutaay m
3
CC BY-XC-8A 3.0
2
3,213
id-0008
IDR0088
(TDR) Robn
CC BY-NC-8A 3.0
25,
idr0009
Simtpson
idr00
ID00000
(DR)
Dmml ml
44,761
5d-0011
DR0011
(DR) Tart)-510-- Pott--ani-----aaae Praaweex & Thorpe 2015
CC BY 4.0
87.89
idr0012
IDR0012
(IDR)
Tuchs 12
1200
CC BY-NC-ND 4.0
22,162
idr0013
R00113
CCO
208,474
idr0017
IDR0017
(IDI) Brroing 06 Sk.
2015
CC BY NC-ND 4.0
65.048
idr0022
IDR0022
(IDR) Kordont 04
2019
CC BY 4.0
54,600
idr0025
DR0025
(TDR)
2012
CC BY-SA 3.0
564
Var
33.707
idr0030
IDR0030
(IDR)
Neto A2 15tkal
201
CC
38 017
id-0033
IR0033
(DD)) Runt 11 Rm
2011
CC BY 4.0
16,024 5
idr0035
IDR0035
(IDR)
2013
CC BY 4.0
11,403
idr0037
IDD
17.617
id:0056
IDRO556
(IDR) Stolte 05 S4.
2012
CC BY 4.0
50,177
idr0069
IDL0069
IIDI) Sidera 120
CC BY-NC 4.0
3
62,812
idr0072
DDR0722
(DDR)
20220
CC BY 4.0
68,642
IROOSO
idr0081
IDR0081
(IDI) Georg)
2020
CC BY
10.00 01.040
idr0086
IDR0086
(IDR) Oriroo de
2020
CC BY 4.0
5,263
idr00s8
IDRO0SS
(IDR)
CC BC
151,021
idr0089
(DD
8,077
5de1093
IDRUDS3
(IDR) Srulin e am
2021
CC BY 4.0
44 858
idr0094
IR00944
IIDR) Piilinerr 2t
CCO 1.0
65,828
idr0115
15
(DDR) Utanka
CC BY 4.0
13.273
IDR0120
Gerauit 2222
34,390
id:0123
IDR0123
(IDR) Tora 05 a1
CC BY
3,157
idr0128
IDR0128
(IDIt U1ssewskj 01
2022
CC BY 4.0
3,755 2
9,539
idr0129
IDRU129
(IDR)
CC BY
19,441
id-0133
IDRO13S 33 (IR) Disiii 05
m1
2023
CC BY 4.0
23.149 2 16.721
id:0140
IDR0140
40
(DRR) 110 01.
2022
CC BY 4.0
idr0145
IDRO145
(IDR)
2025
CC BY
18,338
jump0001
cpg001G.jume
(Cen Pamiing Gallery)
Chandrasckazn
146,741
25 al 2023b
midr0001
ALFI
(Pgchare)
Antonelli et 2023
CC BY 4.0
1
2,146
aid:D002
(Mendeley
2023
CC BY
1,004
B.BIA and B.BAAD&d BBllnng Acchive) Joues
55 aq 2024
Continued on next page
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Continued from previous page
ID
Dataant
Lioense
Channels
Imagee
midz0004
White blood cells (Eigchare) [Bodzas es al
2023:
CC BY 4.0
3
14,565
midr000
BriFiSeg, (Zenodo) (Mathien
CC BY 4.0
1,029
VirtuaSStaining (Pigstare)
midr000?
8 B-BDD000 (Biolmage Archive) Yakunovich Galimov
CCO
31,558
2021
mid:0008
DBBC030 (BBBC) (Koos
2016
CC BY
midr0009
Parasites (Mendeley Data) Zhaing
aidz0010
DCCmmaees (University of Reading Reseaee D.3 Archive)
CC BY 4.0
132
Kempster et al 2022
midr0011
(Gitllub)
CCO 1.0
366
DeepBacsl (Zenodo)
[Sahn
midr0013
DeepBacs2 (Zenodo)
Spaha e1 s.
2022
CC BY
aidr0014
DeepBaca3 (Zenodo)
Spainn
2024
CC BY 4.0
34
aidz0015
EVICAN (Edmond)
CC BY
4,361
Pluorescent
Call (AMSAAC1))
1,809
2024
aidr0017
EIVelell (Pigshare) [Edlund et al 2021
CC BY 4.0
1
5,165
midr0018
Omnipose (OSF) (Cutter
CC BY-NC
791
(BBBC) (Suley munnovs
1,044
midr0020
VISEM-
(2cnode) Fhantosw With Cr
2023
CC BY 4.0
13 608
nidr0021
WBC1 (Mendeley Data) Zheny D1 al.
CC BY-NC 3.0
300
aidr0022
WBC2 (Mendeley Data) Zileing
CC BY-NC
100
BSST263 (Blolmage Ardbivel
mid:0024
CEM5O0K (EMPIAR) (Corrdd & Naravan
CCo 1.0
264,187
aidr0025
S. &.EMMS322221 (BloStiurcs) Raustiennt
2021
CC BY 4.0
2
51
aid:0027
Microscopic peripheral blood (Manddely
Azevedo
CC BY
15.710
aidr0028
Three
Ton annotated potato dataset (Figshare)
Biswas
CC BY 4.0
8
14,269
Harma 2020
aid:0029
Heiser
2020
CC
Rx
(Recursion)
34,757
nidr0031
ReRx1 (Recursion)
Loowaki dt m
A025
Recursion
Custom
26,328
Commercial License
aidr0032
RxRx2 (Recurston) Cuccarese
2020
CC BY-NC-SA
30.744
wtc0001
WTC-11
(Allen Inae
Institute
11
Use
Total 2,792,462
Table A5: Testing Image Dataset Information
Dataset
License
Channela
Images
CHAMMII- WTC
WTC Chen es al.
2023 2024
CC BY-NC 4.0
65,103
CHAMMI HPA
HPA C Beb et al
CC BY NC 4.0
66,938 4
CHAMMI-CP
CP Chen
CC BY- 4.0
86,245 5
CelPPHE
Celn
BY-NC
id:0017
IDR0017 (IDR) [Breinix et 2015
CC BY 4.0
2
122,880
HTAA236226x256
HPAw23 Chen et
CC BY-SA 4.0
1,138,378 4
Jump-
Jump-CD
2023a
CCU 1.0
813.040
MC
BY-NC
123,272
Total 2,474,876
A.2 DATA PREPARATION
Before sampling and data curation, all the data from the original 75 studies was down- loaded to our local servers in their original TIFF JIFE/OME 16-bit formats. We used a high- throughput computing cluster (Center for High Throughput Computing 2006). Condor workflows (Livny et al., 1997). and GNU Parallel (Tange) 2024] for pre-processing and standardization of these datasets, bringing them down to SOTB of compressed images. First, we decoded the original image format (e.g., TIFF, flex, etc) and separated individual channels, individual z-planes, and individual temporal frames into separate files. Next, we standardized pixel depth from the original (e.g., 12 or 16 bits) to 8 bits after rescaling illu- mination values. Images intensities were normalized between 0 and 255 after trimming the tail end distributions of the initial histogram at the 0.1% and 99.9% percentiles. Finally, each single file was independently stored in PNG format with lossless compression, resulting in 422 individual channel files. Note that no spatial rescaling or cropping was used to reduce storage size images in our dataset preserve their original resolution.
A.3 METADATA PARSING
The resulting metadata, file is useful to understand the distribution images and to sample representative subset.. subset. Figure illustatees the diversity of images after selection and sampling
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according to few relevant annotations, which could be leveraged for learning. Importantly, while there are many types of images represented, the specific subgroups in the training set are sparse and have long-tail distributions (Fig. [b). This is known challenge of real world data and an opportunity to investigate robust learning strategies despite the natural biases. Also, note that the original sources may not have all the information of the 22 columns that we tried to parse, due to lack of standards for imaging metadata (Schrnied et al., 2024). Therefore, the final metadata file may have missing information for some studies.
Techniques Used to Reduce the Number of Reagents in the Metadata After merging all meladata sources the resulting file contained 145k reagents, which were reduced to 92k using multiple alignment techniques. We performed metadata harmonization to address heterogeneous identifiers and naming conventions in the candidate list. Using a programmatic gene-ID mapping step (mygene) we mapped ENSEMBL identifiers to HGNC symbols and removed records that were duplicates by virtue of having both identifiers; this eliminated 16,029 ENSEMBL-only redundancies and reduced the working set to 114,748 reagents. We then applied sequence of regex- and rule-based sanitization passes: dis- carded tokens of two characters or fewer, removed purely numeric tokens of three characters or fewer, and normalized hyphenation to collapse format mismatches (for example, MK2206 versus MK-220 (206) while explicitly preserving legitimate hyphens that encode stereochem- istry, numbered loci distinctions (genel-1 versus g1) and organism-specifie conventions (for example, fly/ /woorrm par-1 versus human PAR1). We also stripped timestamp-like exper- imental annotations appended to gene symbols (for example, LUU-90mmn-ILLKS and, when a canonical symbol existed elsewhere in the list, removed the annotated variant entirely. We also usod some databascs such as Flybase, and Pombase datasets rename Drosophila and Yeast related genes.
Ground Truth Extraction for IDR Studies We developed a reprodueible, meta- data-paper pipeline to adjudicate "hit" versus "no hit" labels for candidate metadata reagents using the content present in each paper's reconstructed text, tables, and ma- chine-generated figure descriptions. A datasot-specific prompt encodes the inclusion criteria for what constitutes hit in that study.
The pipeline begins by loading the reconstructed text for a selected study. If image links are present in the text, apply a two-stage figure augmentation pass using OpenAl's ol model (Jaech et al. 2024p. First, we download each image and produce a transcription that captures the image, visible labels and legend text. Second, use the figure transcription to summarize roles, interactions, conditions, and quantitative readouts described in the figure, For each image, the URL in the text is replaced with the generated description that subsoquent steps operate on a text representation that incorporates figure content.
Candidate entity construction is metadata-driven. We read the study's reagent or gene list line-by-line and derive name variants by lowercasing, extracting parenthetical synonyms, and stripping common sulfix words produce base names. We compile word-bounded regular-expresson union of all variants, exclude very short tokens and a small stoplist to reduce spurious matches, and scan the augmented text to recover a de-duplicated set of candidates that are explicitly mentioned in the paper. Because candidates originate from the motadata list and are matched with word boundarics, we limit substring blood (for example, avoiding matches of "ER" within "ERK").
Hit adjudication is performed by invoking paper specific "hit" definitions for each candidate against the full augmented text under lext-only evidence policy. The adjudicator, Ope- nAT's ol model, must both assert whether the candidate meets the inclusion criteria and return a concise rationale together with short, verbatim in-text snippet that substantiates the decision; labels without a snippet do not qualify as positive.
Inclusion criteria are encoded per dataset to reflect the study's stated endpoints. For IDR0017 specifically, a hit is a reagent that exhibits interaction activity with any screened cell line. In all cases, external knowledge is disallowed and every positive must be justified by an in-aa snippet. Outputs consist of a per-reagent record written to a study-specific text. file that captures the final label, brief rationale, and a snippet of at most two sen-
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tences that provides the evidentiary anchor. We additionally persist the full processing log, the augmented text file where applicable, and JSON file with per-figure transcriptions and relationship summaries. Each record includes provenance fields such as the paper identifier, the exact matched string for the entity, model name and version, timestamps, and prompt versions to facilitate audits and downstream benchmarking.
A.4 DATASET CURATION
3D sampling. Seven of the 75 studies involve imaging data acquired in three dimensions, iee, x-planes recorded al various depths. Given that our focus is 2D imaging, we sampled 20% of the z-planes from the central region of the 3D stack. This follows two observations: first, images in the extremes of the stack appear to be empty or out of focus in most cases. Second, z-planes from the same 3D stack are highly correlated and may not bring new information from the 2D perspective. After applying this filtering, we reduced the number of multi-channel images from 26.7M to 24.2M. A6 reports the sampling information for all the 3D studics.
Table A6: 3D Sampling Information for Multi-Channel Images
Datasct Name
Multi-Channel Images
A After
Before
Ratio Preserved
idr0001
741,960
2,374,272
0.3125
idr0011
39,890
167,551
0.2381
idr0086
16,705
76,851
0.2174
idr0089
8,857
40,565
0,2183
idr0115
138,035
579,747
0,2381
idr0120
75,828
192,908
0.3931
idr0123
3,394
13,342
0.2544
wtc0001
39,294
117,882
0.3333
Temporal sampling. Nine of the 75 studics involve time-lapse imaging, i.c., images ac- quired through time. In live microscopy, the camera does not move and the cells do not move loo quickly, resulting in highly correlated 2D frames. Following the same inluitions from 3D sampling, points in given sequence. With this, we reduced the number of multi-channel images from 24.2M to 6.8M. A7 reports the sampling information for all the temporal studies. Table A7: Temporal Sampling of Studies for Chatnel Images
sampled a fraction of the frames by defining evenly distributed time
Datasct Name
Multi-Channer Images
A fter
Before Percent Preserved
idr0002
43,392
386,884
0.1122
idr0011
39,890
39,890
idr0013
959,795
17,848,11 117
0.0538
idr0115
14,815
138,035
0.1073
nidr0003
16,699
33,403
0.4999
nidr0020
14,685
29,370
0.5
Control-based sampling. The majority of studies in the collection involve experiments with control conditions used to compare against treatments. Control samples are usually abundant and replicated multiple times to improve the ability to make meaningful compar- isons. To reduce redundancy, we sampled fraction of the control images to balance its representation with respect to treatment samples. This reduced the number of chantel images from 6.8M to 5.9M.
Intensity filtering. The pixel intensity statistics of microscopy images tend to have a low mean and long tail distribution. We filtered channels whose mean pixel intensity is too dark with respect to the distribution of all channels the same study, under the assumption that these images mostly empty FoV have too small too few interesting objects.
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K-means based sampling. Images in the same study may be too similar to each other because the morphological differences of certain perturbations is too subtle or because some treatments do not have strong effect. In order to sample few representative images from each study we use k-means clustering based on the histogram of intensities. First, we compute the gray scale histogram for each channel with 256 bins of intensity values. Next, we load the bistograms of all images per plate and use kernel k-means with the histogram intersection kernel to group the data in K groups, where K is determined by a sampling factor (pereentage of desired images). For sampling we keep only one random sample from each cluster. This approach ensures diversity in the intensity values of the images, which is proxy indicator of image content. The sampling factor that determine the number of clusters depends on the total amount of images in one sludy: a for studies with less than 100K multi-channel images, 0.5 for studies with images between 100K and 500K images, and a 0.2 for studies with more than 500K images.
A.5 CELL SECMENTATION AND CELLULAR SCALES
We used the Cyto3 model in Cellpose (Pachitariu & Stringer 2022) to segment all the differ- ent 75 studies in our dataset obtain centroid coordinates all these different microscopy images at the single-cell level. Our goal was to segment the nucleus of the cells as that was the most consistent channel type present in all the studics. We manually configured Cell- pose parameters such as size, and channels used for segmentation to improve segmenlation quality. We have found 300 million cells the small version of our dataset and almost. 1.8 billion cells 11,791111,,56 cells) in the large version of our dalaset. Figure Ag provides quantitative background about the segmentation results.
1.8 Billion Cells!
multi-cellular
Colls
collular
Studies
culls abudy:
Median calls study: 2,263, 912.00
Min calls per study: 616
Max calls per stuly: 349, 678. 800
subcellular 90x
Standard devintion: 26.592, 666.
Figure A9: Segmentation pipeline examples.
It takes 7 days on NVIDIA L40s GPUs segment all multi-channel images in CHAMMI-75. Once all images were segmented, we found that the area taken by the segmented nucleus on microscopy images 19. 19.12%, which means that 80.88 of the nuclcus image is empty. To counteract this issue, we used our single coll coordinates to do guided crops during our model training. We also annotated average cell sizes all 75 studies, which helped us observe the cellular structures at different scale levels similar to how magnification levels are discrete in microscopy.
Cell scales. We manually determined crop sizes that contain subcellular, cellular, multi- cellular views and created study-level annotations for the 75 sources. The resolution of some studics may only allow access to to crop and resize images in a predictable way.
two of the three scales. These annotations are helpful
We have developed cellular scale annotations the multi-cellular scale and the cellular scale. These annotations were obtained manually for all the different cellular scale levels. We set the cell centroid for image in the middle and crop the scale accordingly.
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Multi-cellular
Single-cellular
Crop Size
Studies
Figure A10: Histogram of cellular-level annotations
Data loader. In combination, the single-cell coordinates and the scale annotations are useful information to design data loaders that generate fixed-size crops with the desired properties. We implemented data loader that samples image content hierarchically in the following way: 1) randomly select a multi-channel image, 2) randomly select a single cell from the coordinates table, 3) randomly select available scale size, 4) crop region around the cell location with the selected scale size and then resize to a standard model imput size, 5) finally, apply any additional transformations augmentations. This design can be modified or expanded to explore other multi-scale learning approaches.
Content annotations. The heterogeneous images in CHAMMI-75 have different sizes, mag- nifications, and resolutions, which departs from the standard practice of assuming a fixed configuration for all images. This means that a random crop from an image in CHAMMI- 75 may contain multiple cells, single cell, or a subcellular structure. This lack of aligument in scales can be a challenge for representation lcarning algorithms. We created study-lovel. scale annotations for the 75 sources to determine the views available in an image (subcellu- lar, cellular, multi-cellular. We implemented awat,, hicrarchical data loader for training that leverages cell coordinates and scale annotations.
A.6 EVALUATION DATASETS AND BENCHMARKS
IDR-0017. This benchmark aims to reproduce the findings of the IDR017 study: A Chemical-Genttic Interaction Map of Small Molecules (Breinig et al. 2013 The study evaluates gene-compound interactions by treating 1,280 compounds using high-throughput imaging Lechniques. It extracts image-based features at the single-cell level to quantify phenotypic changes under a combinatorial study that aims to understand drug mechanisms and target effects.
12 isogenic cell-lines
The biological experiment behind the images aims to determine whether cell-line (with mutated gene) responds to specific chemical treatments. A control experiment consists of a cell-line without chemical treatment, and then other treatments are applied to evaluate if there is any difference between using the treatments or not. When difference between the control experiment and the effect of a treatment is labeled as a hit. In this study, there are a total of 1,280 x 12 = 15,360 combinatorial experiments which identified 193 hits. To identify hits, the original study used 20 manu- ally engineered features at the single-cell level, including cell size, actin intensity, nucleus intensity, cell shape and nuclear shape.
large enough, the chemical treatment
The dataset consists of approximately 122,880 two-channel images across 96 384-multi- well plates. The two channels are: a nuclous marker and a cytoskeleton marker, and 4 fields of view are acquired per well at 10X magnification in 2048x2048 pixel images. Each plate contains controls followed by treated cells, and the study performed across 2 replicates on 2 different set of plates. The original dataset was obtained from IDR (Williams al. 2017),
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and pre-processed the images to facilitate standardized analysis as follows: 1) Performed cell segmentation using cellpose model using nucleus and cytoskeleton channel. 2) sampled 512 x 512 patch from every image based on cell density using segmentation maps to reduce the number of cells to analyze. This method help us to reduce the average cell count from 2300 cells to 200 cells per image. 3) Patches of 100 x 100 are sampled across single cells for feature extraction.
To transform the original analysis into an evaluation benchmark, we start computing features using trained models to approximate the representation of the phenotypic changes at the single-cell level. To identify hits, we use the model embeddings from images of the same treatment to calculate the effect size with respect to control cells. The single-cell embeddings are aggregated by taking the mean per image and two Fucledian distance matrices are constructed: one for the treatment vs control images, and another one for the control vs control images. The effect size of compound is estimated using the Wasserstein distance between the distance matrices of control and treatment image embeddings. To avoid batch effects, we aggregate both replicates for each compound using PCA whitening normalization. The compounds for each cell line are ranked on the basis of the effect score. We use Recal1@50, Recall@100, 100, and AUROC as metrics to measure how well a model ranks high- interaction compounds above low-interaction compounds.
HPAv23 at 256x256. The HPAv23 subcellular benchmark was released with the SubCell model Gupta et al. 2024). The dataset was originally curated in the 23rd version of the Human Protein Atlas Project (Thul et al. 2017]. The authors of (Gupta et al.) 2024) made crops of the images to allow machine ingestion, and develop a benchmark for the same. This benchmark provides a granular detail needed to test whether current models can identify meaningful subcellular differences in cellular organization.
The dataset is collection of immunffuorescence images encoding the expression and spa- tiotemporal distribution of 13,141 genes in 37 cell lines. The images were stained with DAPI, fluorescent dye that labels the mucleus, and antibodies labeling endoplasmic retic- ulum (ER), microtubules (MT) and protein of interest (protcin). The images were cropped from FOV images to single cell images that contained 1,78 cells. The data set was split by the original authors based on antibodies with a ratio of 7: 1: 2 into training, validation, and test sets, respectively. (Gupta et al. 2024 used multilabel stratification strategy to ensure a similar multilabel distribution between the sets.
The protein localization task we have adapted paper is supervised learning task using a multi-layer perceptron (MLP) on the features obtained from a frozen backbone of all the models. The MLP classifier uses the same three-layer classifier architecture as (Kraus) ot all 2024) and focal loss (Cho et al.] 2022) to address class imbalance in the datasct. The classifies were trained on leatures extracted from the whole HPAv23 set including the pre-training set, and the rest of the images. Unlike the original authors, we downsampled all images from 1024x 1024 to 256 to accelerate the benchmark, reduce its time complexity, and decrease storage requirements. We are re-releasing this reformatted set with our dataset to enable easy usage of this benchmark. The task has two sets of classifier: the first set us comprised of 19 categories specified in Kaggle challenge, and the second set has a broader range of 31 categories. We have reported the micro and macro average precision (AP) as the classification metrics and used multilabel ranking average error and coaverage error to evaluate multilabel performance on the test sets just like the original authors. The challenge category results are reported in the main text in and F16 category are reported in F17
The results of the unique
JUMP-CP1 Compounds. In this benchmark, the quality of the replicate level and con- sensus treatment profiles is evaluated in a subset of the JUMP-CP1 dataset (Chandrasekaran et al] 2023bb for the compounds that were originally curated with Broad Institute's Drug Repurposing Hub. Cell Painting assay (Bray et al., 2016) was used, where six fluorescent dyes highlight eight cellular compartments that are imaged in five channels 20X magni-
fication.
From a biological perspective, the aim is to capture meaningful differences between popu- lations of cells with respect to the perturbation and the target of this perturbation. Those
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differences between cell states might be subtle and hard to detect for certain types of pertur- bations even against negative control (unperturbed) cells. The quality of the replicate level profiles is defined by the closeness of given perturbation replicates against a set of negative control replicates. The quality of treatment-level profiles is evaluated by the closeness of profiles with the same biological label (in this case, gene target) against other perturbations. Both metrics are introduced and implemented in copairs benchmarking suite (Kalinin et al. 2025
Originally, JUMP-CP1 includes many biological and experimental conditions: two cell lines (A549 and U2OS), three perturbation types (chemical compounds, gene open reading frame (ORF) overexpression and CRISPR-Cass knockouts) captured at two time points. For this benchmark, we used only the data from the U2OS cell line and chemical compound pertur- bations at both time points, eventually having similar set of seven 384-well plates that matches the one used the evaluation of SubCell (Gupta et al. 2024b. The images from the evaluation plates are not used in the pre-training set. Original 16-bit TIFF images 124,192 fields of view 120,960 single-channel images in total) were normalized and com- pressed to 8-bit PNG images with DeepProfiler (Moshkov et al., 2024) prepare option and then single-cell crops were exported with export-sc option, using the cell locations provided with the original CellProfiler features, resulting in 2M unmasked cell-centered crops ("cells in context").
In this benchmark, we start by computing features for single-cell crops that are saved at size 160x160 and that are further cropped to size 128x128. The final 128x128 input images are resized to accommodate to the expected input size of the particular model at use. Single- cell feature vectors are then aggregated using mean aggregation to the well-level (replicate) profiles. Well-level profiles are then batch corrected with ZCA-whitening from pyCytominer package (Serrano et al., 2025) relative to negative controls with epsilon = 0.001. This data then used in the benchmark: copairs returns mAP and p-value for each perturbation (phenotypic activity) and target (phenotypic consistency). We report the mean mAP for phenotypic activity as JUMP-CP1. For treatments that did not pass the 0.05 p-value threshold, we assume mAP 0. We report mean mAP for phenotypic consistency as JUMP-CP2. Similarly, for targets that did not pass 0.05 p-valuc threshold, we assign mAP = 0. As phenotypic consistency is calculated only for treatments that passed p-value threshold for phenotypic activity, some targets might not be represented in this step of benchmark. We also assign mAP to such missing largets.
CelIPHIE. This study used iPSC-derived neurons to investigate genetic and morphological markers of Huntington's Disease using the latest generation of pooled genetic perturbations at the single-cell level with a multiplexed optical screen. The dataset contains a total of 57,021 images out of which 45,782 training images and 11,239 testing images. The original images from the dataset were filtered, and we kept images from DS28 time sample, and we removed two genes from the original set which were DGKE and GAS7 genes. Single-cell images segmented, cropped and masked al 64x64 pixels. The imaging panel consists of 5 Cell Painting channels and protein markers obtained with immunolluorescence, for a total of 14 imaging channels. The 14 channels in the images are in the following order: DNA, NeuN, pRPS6, RANGAPI, NFKB, TOM20, LAMP1, TDP43, G3BP1, GM130, Golgin97, SYTO, ER, AGP. Channels to 10 are protein channels.
Each single cell was perturbed with one of 19 genes, which was identified with optical is non-targetting control used a reference
barcoding. From the 19 perturbations, to determine the effect of other preturbations. The task in this benchmark is to classify single cells in a binary classification setting: non-targeting vs perturbed gene. The dataset has a training / validation split. to evaluate performance using a linear probe. Note that none of the images in CellPHIE were included in the CHAMMI-75 training set, representing a fully held-out set with novel channel combinations and the largest mumber of channels in the benchmark.
RBC-MC. This benchmark is based on bright-field microscopy imaging of red blood cells (RBCs) from Doan et al. (Doan et al. (020) containing one channel. The dataset comprises RBC samples collected from two geographically distinct blood banks located in Switzerland and Canada. Following the experimental protocol, we train a logistic regression classifier on
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one dataset and evaluate its performance on the other to assess cross-datasel generalization.
The benchmark consists of 76,577 images from the Swiss dataset and 46,695 images from the Canadian dataset, with all images uniformly sized at x8x pixels. RBC morphology is categorized into seven distinct classes: Smooth Disc, Crenated Disc, Crenated Discoid, Cre- nated Spheroid, Crenated Sphere, Smooth Sphere, and Side View. Images originally labeled as "Undecided" by expert annotators, indicating uncertainty in morphological classification, were excluded from the benchmark ensure label quality.
A.7 METADATA COLUMNS AND DESCRIPTIONS
Our metadata fields are divided into six different groups, where each group corresponds to particular type of experiment. The metadata comes in six major groups: experiment, biology, imaging, microscopy, geometry, and storage information. Each record in the meta- data file points a single channel file. The metadata designed to facilitate grouping of channel files according to the categories described before. For cach category, we have several metadata columns described below. If the information for an image is missing or not known, the corresponding value will be labeled with the string "unknown". We try not to leave NaN or emply strings in the metadata file. If you see something, say something. A1l contains visualization of the six groups of metadata and the 22 fields present in the meladala.
We are going to provide a detailed list with descriptions for all the different columns present in the metadata:
1. experiment.study: Identifier of the study.
2. experiment.plate: Plate where the image was acquired. If images come from another format (not plate-based), this identifier can indicate a major group of ex- perimental arrangements the study.
3. experiment. well: Well position within the plate. The format of letter and number is preferred, but this is flexible.
4. experiment.reagent Tdentifier name of the treatment reagent used to treat the cells. In many cases, this is a gene name, a compound name, or a protein name, while in other cases it may reflect other experimental interventions (e.g., temperature).
5. experiment.control. Whether the image comes from a control well or not, and what type of control they may be, for example, positive or negative control. If not a control, use the string "no".
6. biology.organism: Name of the organism where the cells come from. For example, humans, mice, plants, etc.
7. biology.cell line:
Name of the cell line. Many cell lines have well-known names (such as HeLa)), other cell lines are from primary patients and have anonymized codos, and others are from genetically modified organisms.
8. biology.cell _tpe: The functional type of cell, regardless of the cell line. Exam- ples include neurons, red blood cells, cancer cells, pancreatic cells, etc.
9. imaging. multi caaitti__hannee____e id: This the field that ties together multiple chan- nels. It is consecutive number from the original database concatenated with the study number. A unique multi_channee chwnnel_ineiaaeeee connects the channels of an image.
10. imaging. :anee: Names and dyes of the channels used to create the image. This gives context for where the observed channel file comes from. Example: "DNA, protein, cytoplasm".
11. inaging.channel. Numeric value of the channel according to the panel. This value is one-based.
12. imaging.channe type: Biological compartment of the cell that is visible in the channel. This a list of standardized values that include: nucleus, cell body, frilh,-field, etc.
13. microscopy.typp: Name of the type of microscopy used for acquisition of the channel file. Examples include: fluorescence, bright-feedd confocal, cryoEM, etc.
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Imaging multi_channel_id
Storage path
Geometry
Experiment
panel
study plate
channel channel Ltpe
width
height
depth
well reagent control
Biology
Microscopy
channels
organism
lype
z_slice timepoint
cell_line
magnification field_of_ _f___eewww
coll_tpe
Figure A11: Visualization of metadata fields in six groups and 22 fields.
14. micrescoyy.manniication Numeric value of the magnification used to acquire the image.
15. microscopy.fov: Field of view, well site, or microscope position the well when the channel was captured.
16. weotetry.width: Channel width in pixels.
17. geometry.height: Channel height in pixels.
18. geometry.depth: Total number of z-planes this channel belongs to, if the study is a 3D imaging assay.
19. geometry.channels: Total number of sibling channels in the same image.
20. geometry.z_slicne Number of the z-plane for this channel. It is a numerical value.
21. geometry.timepoint Number of the frame in the timelapse sequence, if applica- ble.
22. storage.path: File path of the PNG file in the dataset containing this channel
A.8 METADATA QUALITY AND HANDLING INCONSISTENCIES
The construction of CHAMMI-75 involved integrating 75 heterogeneous studies, many of which lacked standardized or complete metadata, reflecting a general deficiency in data shar- ing practices across biology. Our approach handling missing, inconsistent, and ambiguous data was rigorous yet pragmatic, acknowledging that perfect retrospective standardization often impossible.
Handling missing data. The vast majority of studies provided partial metadata. For fields where information was unequivocally absent (e.g., bhology.cell_typp mi- croscopy.fov), the entry was explicitly and consistently annotated with the string "unknown") This approach avoids using null values or empty strings, ensuring consistency for subsequent programmatic access and filtering.
Resolving ambiguity and inconsistency. In cases where metadata was ambiguous (e.g,, multiple spellings for a cell line, or an overly verbose stain identity), we employed multi-step resolution pipeline: (1) Cross-reference: values were validated against other metadata columns (e.g., cross-checking organism and cell line). (2) External validation: we performed targeted web searches and reviewed the associated scientific publications infer the most likely accurate value. (3) LLM-assisted parsing: As noted in Appendix A.3 we used LLMs to systematically extract and organize certain information, providing a rapid, initial pass at resolving naming variants and extracting structured data from free-form text descriptions. (4) Manual curation and normalization: to minimize inconsistencies, all resulting values were manually reviewed, low-cased, and mapped to simplified vocabulary or ontology where possible (e.g., different fluorescence channels like 'DAPI, 'Hoechst 33342', and 'Nuclear stain' often mapped under the canonical channel type of 'nucleus").
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Last resource. If, after all these steps, the interpretation remained uncertain (e.g., stain was ambignously described and the original publication offered no clarity), we defaulted to preserving the original value or, more conservatively, labeling the entry as "unknonn".
The resulting metadata table, while significantly cleaned, remains inherently noisy. We view this noise and high sparsity (Figure 4b) not limitation, but as defining feature of the resource. Unlike highly standardized datascts producod synchronously by a single consortium, CHAMMI-75 attempted to aligu data that were never intended for cross-study integration. This noisy complexity presents the real-world challenge foundation models for bioimaging must solve learn representations that are robust despite inconsistent. Future efforts should focus on promoting standardized metadata acquisition for new studies (Schnied et al. 2024), as retrospectively fixing metadata. is computationally infeasible and prone to subjective error. Furthermore, our dataset provides compelling target for future research into semi-autonated, multi-modal systems capable of resolving these remaining metadata inconsistencies.
A.9 COMPARISON TO OTHER DATASETS
These datasets were chosen for comparision rather an Al-ready dataset to investigate single-cellular morphology foundation models. Our data set is not reference set for interactive querying because contains diverse samples from heterogeneous studics a way optimized for machine learning, not for biological discoveries. We have compared our data set against similar imaging data sets that ready for machine ingestion and have been used in relevant work to build foundation models in cellular morphology. All the numbers used in Figure 2
dataset is not biological study but
are reported in the table A8
Table A8: Overview Multi- Channel Image Datasets.
Name
Images
See
Ch.
Org.
Access
Cite
2,840,483
Mulai-channel
CHAMMI
220,284
3
Multi-channel
Public
Chen et al.
2025
Rxlxx
4,168,99
exedd-caaneel
Public
Itecursion
Jump-CP
8,109.884
Fixed-chennel
Chandrape karan
2023n
HL'Ax23
1,138,378
Fixed channel
Public
(Guppa es al
Multi-channel
Bourriez
Pheeooriiat- 16M
16,000.000
Fixed channel
Private
Kenyon Dean et ST.
2021
Cytolmageneet
890,737
12
Mixed-cheannel
Public
Microsndop
2,200,000
Xuu al.
Src refers to Source. Org. refers to the type of organization of the dataset and whether it was multi-channel, fixed-channel or mixed-channel. Multi-channel means that has images with images having varied number of channelled images, Fixed-channel means that the images only have one configuration of channels. Mixed channel mcan if they originally had images with varied number of channels but the channel order and information was not preserved properly.
B FACTORS AFFECTING REPRESENTATION LEARNING
B.1 MOTIVATION
CHAMMI-75 is highly heterogeneous (Figure 4 In this section, we aim to understand how different factors of variation drive generalization representation learning. We perform five ablations using metadata entries as a signal to separate data with for training. The factors are the following:
Specialization
Modality
Magnification
Cell Line
Similarity to HPA
Number of Channels
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Raleiee paroe perserlag di difennoe willnn each sludy
CHAMS uccre
WFG scao
WiA s0or
CP scors
CHAMMISCa'
MTG soxx
IPA scors
GPacas
0/420
08172
13583
900%
3744%
0.317
1389
2306
0-190
0.687
1807
1386
0385
685
404
Callee
0.19
0.660
1964
208%
4.0%
1.80
1708
0.699
0.981
1900
1389
647
618
4.9%
3/5
0388
1400
1300
637%
676
1816
Amero
Figure B12: Expanded version of Figure where show CHAMMI scores numbers and relative percentage differences within each ablation
All these experiments have been conducted on the best performing model configuration we found in our scalability analysis (Figure 8): DINO BoC (Bag of Channels). Tn our ablation experiments, we compare the two most dominant metadata entries for each column removed against the remaining diverse set of entries left.
B.2 SPECIALIZED MODELS ABLATION
We compared our CHAMMI-75 pre-trained BoC model against specialized self-supervised models trained on training sets (as reported in (Chen et al. 2023 of all the three datascts: WTC, HPA, and Jump-CP.
According to Figure B12 we see 38% increase when we use the diverse CHAMMI-75 to train a self-supervised learning model. This result inspired us to conduct the ablation study to better understand what factors enable this significant boost in performance. We have found variability in the amount of effect these different metadata parameters have.
B.3 MODALITY ABLATION
We separated the data into two separale sets: the two most dominant modalities (fluo- rescence and epifluorescence modalities) versus all of the rest. The pre-trained model on fluorescence imaging consists of 278,735 multi-channel images (880,000 single channel im- ages), whereas the pre-trained model of other modalities consists of 281,823 multi-channel images (790,000 single channel images). We see the distribution of multi-channel images in Figure B13 where the other modalities have the majority of studies with 4 channel types.
According to our findings in Figure 8 one would expect the fluorescence imaging model to perform better than the other modalities model due having more number of images but our findings contradict this expectation. We that the other modalities model performs 13% better than the two modalities model, demonstrating that modalities play huge role in affecting representation learning. We can infer from this result that having diverse microscopy types helps the model to learn better representations and hence is an intogral feature of the datasct. These results align with previous work by (Arevalo al. 2024), who identified microscopy type as the dominant confounding factor in batch correction studies. This convergent evidence suggests that exposure to diverse imaging modalities drives the learning of robust, generalizable representations that transfer effectively across different imaging contexts
B.4 MAGNIFICATION ABLATION
We separated the data into two separate sets: the two most dominant magnifications (20 and 63) versus all the rest. We pre-trained DINO bag of channels at a fixed compute budget these two sets of data understand how different. sets of data affect representation
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d
Mode
Modld Thaned un UAM
CHAMN Score
Modality
Checkootts
Channele
checkssiee
Uheckpoins
Figure B13: Showcasing Modality Ablations of having 2 modalities versus 13 modalities compared to the Specialized Models and model trained CHAMMI-75
EaKagnffcatioons Mognniccctions
Spesialized Model
Model
CHAMMI Score
WTC
Chackporti
Chscapentts
Midia
Checkporih
Oreckpannt
Figure B14: Showcasing Magnification Ablations of 20 and 63 compared to the Specialized Models and model trained on CHAMMI-75
learning. The distribution of channels (shown in Figure B14p) in the top 2 magnifications
skewed towards studies with (> 3) higher channel types whereas it is the latter in the 13 magnifications. In Figure B B14b p, 13 magnifications had different modalities, but top maguifications were dominated by two modalities: fluorescence microscopy and confocal microscopy.
We compared the CHAMMI scores and observed in Figure B14 that we see marginally better performance in the case of diverse magnifications by 2.5%. These results indicate that diverse magnification slightly improves performance.
B.5 CELL LINE ABLATION
We separated the data into two separate sets: the two most dominant cell lines (HeLA and U2OS) versus all of the rest. We pre-trained DINO bag of channels at a fixed compute bud- get these two sets of data to understand how different sets of data affect representation learning. In Figure B15, we see what the distribution multi-channel images looks like in terms of channels, modality, and cell lines. We
that in terms of the modality distri-
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2CellLines
RestAllCellLines
Specialized Model
Model Trained CHAMMi-75
Cicckpunnts
Chockpoiii
Checkpaints
Showcasing Cell Line Ablations of U2OS and A-459 compared to the Specialized Models and model trained on CHAMMI-75
Figure B15:
bution in Figure B15b, two cell lines consist mainly of fluorescence and confocal microscopy. The distribution of the number of channels in Figure B15h these studies scems to be fairly equally distributed as the rest of the cell lines.
We compared the CHAMMT scores and observed in Figure B15 that marginally better performance in the case diverse cell lines by 1%. These results indicate that cell line variation does affect performance.
B.6 HPA CURATED ABLATION
We investigated how training data similarity to the target evaluation set affects represen- tation learning. Our methodology involved extracting features from 1,000 single cells per study and channel type, totaling 75,000 cells across our dataset. We performed identical feature extraction on the CHAMMI-HPA test set using our best-performing DINO BoC model. We quantified similarity between different channel type studies against the test day. The average feature vectors were computed for each study and channol combination and then the top 10 most similar studies and bottom 10 most dissimilar studies were selected. We trained separale self-supervised DINO BoC models on each subset. on a fixed compute budget.
In Figure B16h, we can see that the most similar studies arise from studies with channels which makes sense НPA also contains 4 channels. We see clear demarcations between different modalities and cell lines in B16b,c into two separate succint groups: one similar to HPA dataset, and one dissimilar to it. We compare the CHAMMI score results and improvements in the HPA benchmark suggesting some benefit in training with similar data but overall the performance decreases in WTC, CP resulting in an overall net zero affect on CHAMMI score. This finding indicates that diverse, dissimilar training data promotes learning of more generalizable representations, even means sacrificing some degree of specialization to the target domain.
This result reinforces our broader finding that diversity in training data is a key driver of robust representation learning. Models trained dissimilar data are exposed to wider range of biological and imaging contexts, which appears to facilitate the learning of features that transfer more effectively across diverse microscopy tasks. While sppeif- fine- tuning on similar data may benefit specialized applications, diverse pre-training appears essential for building generally applicable microscopy foundation models.
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1Rd Tarss CS0R66
sidaiaa IRNARIC SaTT seppiaarn CINOONE Sentre
WTCC
Chadiprtts
HPA
Get B2300
Checkpatts
Checppoirts
Figure B16: Showcasing Similar Data Dissimilar Data compared to the Specialized Models and model trained on CHAMMI-75
B.7 CHANNEL NUMBER ABLATION
We investigated whether the number of imaging channels present per study influences rep- resentation quality. We trained DINO BoC models on study subsets with varying channel counts (2, 4, and channels). Data volume and Channel count two confounded vari- ables as when you add studies with more channels, total data volume also increases. This results in seeing similar results as dataset scaling shown in Figure you don't control for this confounding variable.
To control for this confounding variable, have fixed compute budget for all models at 500,000 training iterations. Under these controlled conditions, we observed no substantial performance variations attributed to channel count (Figure [17). This result is expected given that the BoC architecture does not explicitly model inter-channel interactions, treating each channel independently.
Whether modeling inter-channel dependencies could yield improvements over BoC ap- proaches remains an open research question. While several methods have explored this direction, including (Bao et al. 2023), (Bourriez et al.) 2024), and (De Lorenci et al.) 2025) approaches, none have yet. deroonstrated clear superiorily over BoC methods. Our dalaset, with its diverse channel configurations across studies, provides valuable resource for future investigations into this question.
C SCALABILITY ANALYSIS DETAILS
To train multi-channel ViTs, prior work has explored supervised (e.g., Channel-ViT (Bao all 2023)). self-supervieed (e.g., CA-MAE (Kraus al. 2024), ChADa-ViT (Burriez et al. 2.24)), or hybrid multi-task objectives (e.g., ChA-MAEVIT (Pham et al. 2025 CHAMMI-75 is a pre-training dataset primarily useful for self-supervised, potentially useful for weakly supervised, and not useful for fully supervised learning. We explore SST, algo- rithms train a multi-channel model using CHAMMI-75 with the goal of learning cellular morphology representations for solving diverse downstream tasks. Here, evaluate well- established, representative algorithms from three major families of self-supervised learning methods for images: (1) SimCLR (contrastive) (Chen et al. 2020 (2) Masked Autoencoders (reconstructive) (He et al. 2022 and (3) DINO (self-ditillation (Caron et al., 2021).
pre-training resource, compare performance by training with varying configurations. First, we evaluate the performance of models trained with increasing amount of multi-channel images, which call " "datased scaling" evaluation.
To evaluate CHAMMI-75
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Hede 25
Figure B17: Showcasing Channel number variations with fixed compute of 500k compared to the Specialized Models and model trained on CHAMMI-75
Performance evaluated on the CHAMMI benchmark (Chen et al. 2023 which has six tnt-of-distribution, phenotype matching tasks. We train models liecialized lized on fixed chan- nel configuration as a baseline (1ds, 33K images per dataset; 3,4,&5 channels), and then combine sets to increase channel types and source variation, starting with the 3 CHAMMI subsets (3ds, 100K images, channels), 10 datasets (10ds, 178K images, 14 channels). and a sample of the images in the 74 pre-training datasets from CHAMMI-75 (74ds-small, 560K im- ages, 25 chamnels). We leave the full CHAMMI-75-set (74ds-large, 2.8M images, 25 channels) out of the scaling evaluation; ing the best configuration in our analysis. We also conduct a "model scaling" evaluation, where we keep the dataset size fixed (74ds-small) and grow model paramcters.
only used it for the main benchmarking experiment follow-
Experimental and implementation details. We train two types of ViT models: bag of channels (BoC) and multi-channel attention (MCA). The input size for all models is 224x224 pixels, and the number of channels is either one (BoC) or variable (MCA). Models are trained with one the three selected SST. algorithms (SimCLR, MAF, DINO), and we keep prper-parameters constant across experiments as possible and apply relevant tweaks to avoid optimization divergence or collapse. After a model is trained with SSL, we keep the weights frozen and extract features in the three test sets of the CHAMMI benchmark without any finctuning. We use the PyTorch framework and we run experiments using multi-GPU training with to 8 GPUs typically in single node. All experiments were conducted in academic compute clusters (Appendix G.2 h.
Results. Consistent. with previous studies, find that models trained with SSL benefit from having access to more data (He et al.) 2022 Oquab et al. 2023: Siméoni et al. 2025 We compare performance against the top-line of specialized models trained for each of the three subsets with fixed channels and with full supervision. This serves as an upper- bound reference to assess performance of models trained with SSL. The results are presented in Figure 8 and confirm that as we increase the dataset size and the model size, SSL models approach the performance of specialized, supervised models. This indicates that single, multi-channel model trained al scale without supervision can be highly competitive various downstream tasks.
We observe that the top factor that determincs performance is the multi-channel strategy (BoC or MCA), followed by type of SSL algorithm, and model size. BoC models yield better performance than MCA models in the SSL regime, indicating that cross-channel correlations remain difficult to learn without supervision. We find BoC models casier to scale than MCA models because the latter has longer sequences that have high memory and compute requirements. MCA models required between 3X and 5X more GPU hours than BoC to complete training session with the same amount of data (Figure G23 Tn addition,
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SSL algorithms perform comparably within multi-channel strategies (BoC MCA), with DINO consistently outperforming the others.
Based these results, trained BoC ViT-small model with the DINO algorithm using the full CHAMMI-75 dataset, which has 5X more data than the 74ds small set, and required 2,352 GPU hours to complete (7 days with 2x7-GPU servers). The result of this experiment followed the performance improvement trend obtaining a 9.8% relative improvement over the best result in the dataset scaling evaluation. The model scaling results suggest that additional performance gains could be obtained with larger ViT architectures, which we did not explore in this work.
D QUALITATIVE ANALYSIS OF FEATURE SPACE
To visualize the global structure of the feature space learned by models trained on CHAMMI- 75, we performed a qualitative analysis using UMAP projection on single-channel features extracted from a balanced, single-cell sample of all 75 source studies.
D.1 EXPERIMENTAL PROTOCOL
We sampled approximately 1,000 single-cell, multi-channel crops from imges in each of the 75 source studies, resulting in 196,660 individual single-channel images. We then used three representative pre-trained VIT models to compute fixed- fixed-length feature representations for each individual single-channel image: (1) CHAMMI-75 DINO-BoC: our proposed model trained with CHAMMI-75 (2) DINOv2: the generalist vision model adapted for microscopy features. (3) OpenPhenom: the channcl-adaptiee model trained on fxee-channel Cell Paint- ing data. Given the difference in number of channels, we do not display multi-channel images in the visualizations because the bag-of-channels approach yields a variable-length feature representation across studies. We collect single-channel features with all models, including the channel- adaptive OpenPhenooe model.
D.2 RESULTS AND DISCUSSION
The UMAP visualizations for all three models (Figure D18) show that the feature space is primarily segregated by the source study ID (technical domain), rather than being uni- formly mixed. This result leads to two key interpretations: (1) domain sensitivity vs. in- variance: self-supervised models, even when trained on highly diverse data, do not produce strictly
representations in the raw feature space. Instead, they are domain- sensitive, clustering the imput based on technical factors (e.g., image acquisition parameters, microscopy type, local processing) unique each of the 75 source studics. This strong inter- study differentiation highlights the genuine heterogencity of CHAMMI-75 with quantitative results: this qualitative obscrvation aligns with our findings in the Dis- entanglement Experiments (Appendix ); since the technical signature remains present, post-pre post-processing step like batch correction is necessary to remove this study-specific bias and maximize the detection of subtle biological signals. The strong clustering visible in the UMAP indicates that the high performance CHAMMI-75 features (Section 5.1) achieved not by erasing the domain difference entirely, but by encoding these differences in way that is highly distinguishable and amenable to correction. Further qualitative exploration by coloring the UMAP plots with other metadata fields (such as channel configuration or biological condition) reveals strong intra-study consistency, but the inter-study structure remains dominated by the technical source.
(2) Consistency
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DINO-BoC with CHAMMI-75
DINOv2
Study
15.0
10.0
0.0
OpenPhenom
5.0
15.0
UMAP: ne reighbore-11, min detto..3
-0.0
Figure D18: UMAP visualizations the feature spaces obtained with three ViT-small models: DINO-BoC trained with CHAMMI-75 DINOv2, and OpenPhenom. The colors indicate source study, which is the dominant factor of variation in all three cases.
E DISENTANGLEMENT EXPERIMENTS
To quantitativoly assess the quality the learned representations in separating biological signal from technical noise, adopt the batch correction benchmarking framework from (Areallo et al. 2024). This framework is designed to evaluate how well a feature set dis- entangles batch signal (confounding variable) from biological signal (compound identity) by measuring metrics before and after the application of slate-of-the-rd batch correction algorithms. A high-quality feature set should: (1) require minimal correction in its raw state, and (2) maximize biological signal post- corsecorrectinn
F.1 EXPERIMENTAL PROTOCOL
We evaluate performance using Scenario 2 from (Arevalo et al. 2024 302 landmark compounds generated by three different laboratories using the same micro- scope type. In this scenario, the batch variable is the Laboratory ID and the biological variable is the Compound. We assess the profiles using the population-averageed well-level approach to compute profiles. We compare three feature models: (1) CellProfiler Bascline: The original raw morphological features extracted using the conventional image analysis pipeline. (2) IDRCell Features: Features extracted using ViT-Small DINO-BoC model
the classification of
trained on the smaller, heterogeneous IDRCell 100K dataset (~ 100K images). (3) CHAMMI-
75 Features: Features extracted using our proposed ViT-Small DINO-BoC model trained on the full CHAMMI-75 Large dataset. Based on the original study's findings, we focus on Scurat CCA the optimal batch correction method for this scenario, which we apply to correct the features from all three models. Following standard practice, we report the ag- gregate score four batch correction metrics, and six biological metrics, and the detail of these metrics is also reported for all three evaluated methods.
E.2 RESULTS
The main results reported in Figure E19 highlight the following trend: (1) baseline state (no correction): CHAMMI-75 features exhibit the best performance at the baseline level, yielding higher biological signal, demonstrating better initial disentanglement of batch effects
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Baseline
B
Seurat CCA
C
Features
IDRCell
Compound
CHAMMI-75
Bio Metric
Correction Method
Baseline
Seurai CCA
Batch Metric
Source
A,B
Compound
D04887
Quinidine
013855
055401
102083
D02035
033955
000080
105462
D01890
022771
073628
Figure E19: Disentanglement of feature representations using batch-correction on the JUMP-CP dataset, following Scenario 2 from the framework by (Arevalo et. al., (024). A) UMAP visualization of raw DINO-BoC features learned from CHAMMI-75 and aggregated at the well-level. B) UMAP visualization of corrected DINO-Bot features from A corrected with the Seurat CCA algorithm. C) Scatter plot of batch and biological scores (x and y axes, respectively) for three feature representations: IDRCell in red (ViT-small trained with IDRCell dataset), CHAMMI-75 in blue (ViT-small trained with CHAMMI-75 ), and CellProfiler in green (classical features). In both axis, higher performance is better.
compared to both baselines. This indicates that pre-training on the highly diverse and cu- rated CHAMMI-75 dataset yields representations that are inherently more robust cross-site technical variation. (2) Post-correction state: after applying the Seurat CCA batch correc- tion method, CHAMMI-75 features continue to facilitate the best performance, resulting in the highest biological signal among all feature sets. The performance of CHAMMI-75 features 1.7% better than CellProfiler fcatures, and 11.8% better than IDRCell features, all after correction. Detailed results of batch correction metries reported in Figure E20 for the CHAMMI-75 features, Figure E21 for IDRCell features, and in (Arevalo et a.. 2024 for CellProfiler features.
This confirms that the learned CHAMMI-75 feature space is structured in a way that is amenable correction, allowing the batch correction algorithm to remove confounding noise without destroying the underlying biological coherence. Importantly, features learned with the IDRCell dataset consistently perform worse than the CellProfiler baseline in both the raw and corrected states. This contrast highlights that simply aggregating diverse data insufficient; the scale, comprehensive heterogeneity, and careful curation embedded in CHAMMI-75 are necessary factors for learning disentangled, biologically meaningful repre- sentations.
F BENCHMARK RESULTS
We will take look larger resolution of numbers for all the benchmarks with more related results for better analysis.
F.1 CHAMMI
CHAMMI the benchmark for multi-channel microscopy imaging presented in (Chen et 2023). Here, we show all the values for CHAMMI showcased for dataset scaling laws in F9 We show sub-scores for WTC, HPA, and CP in F10
F11 and F12 respectively.
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Batch correction
Aggregate
Method
cornettvity
KBET
batch
label
ARI
Silhouette
mAP
(nonrep)
metrics
Overall
Seurat
0.61
0.81
0.48
0.49
Scanorama
0.54
0.66
0.66
0.74
0.98
0.05
0.46
0.47
0.93
0.47
0.65
0.56
0.60
Harmony
Sourat RPCA
62
0.52
0.42
0.79
0.98
0.05
0.48
0.49
0.94
0.48
0.59
0.57
SCVI
0.48
0.47
0.06
0.40
0.93
0.48
0.57
fastMNN
Combat
0.61
0.15
0.12
0.69
0:98
0.03
0.45
0.49
0.92
0.43
0.39
0.55
0.49
MNN
0.16
0.45
Baseline
0.91
DESC
0.40
0.53
0.30
0.54
0.97
0.03
0.37
0.32
0.87
0.39
0.44
0.49
0.47
Spharing
0.55
0.4
1.0
Figure E20: Detailed report of batch correction metrics for the DINO-BoC model trained with CHAMMI-75
Batch correction
Method
connectivity
KBET
batch
batch
label
ARI
MMI
label
(controls]
(nonrep)
metrics
Overall
0.80
0.36
0.90
Scanorama
0.46
0.75
0.65
0.78
0.98
0.02
0.32
0.41
0.88
0.36
0.68
0.49
0.56
Harmony
0.57
0.54
0.43
0.82
0.97
0.03
0.34
0.43
0.89
0.37
0.59
0.50
0.54
Souret RPCA
55
0.54
0.34
0.97
0.03
0.35
0.43
0.90
0.36
0.55
0.5
Combat
0.51
0.19
0.06
0.72
0.97
0.02
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0.88
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0.44
Baseline
0.26
0.05
0.30
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Sphering
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0.97
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w...............................
DESC
0.25
0.62
0.28
0.35
0.41
Score
Figure E21: Detailed report of batch correction metrics for the DINO-BoC model trained with IDRCell
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Table F9: Summary of IID Mean, CHAMMI Scores for WTC, HPA, and CP sets
Model
IID Mean
CHAMMI Score
WTC
HPA
CP
MorphEm (ours)
84.26
48.75
61.72
55.84
28.71
OpenPhenom (ViT Small)
75.55
38.23
42.80
43.15
28.74
DINOv2 (ViT Small)
73.95
37.93
46.27
39.66
27.87
IDRCell (ViT Small)
68.55
37.38
45.29
40.40
26.44
Table F10: WTC Dataset Benchmark Scores
Model
Task 1
Task 2
MorphEm (ours)
72.56
61.72
OpenPhenom Phenom (VIT Small)
59.72
42.80
DINOv2 (ViT Small)
63.56
46.27
IDRCell
56.33
45.29
MCA-SSL (ViT Small) ours
60.16
44.70
Table F11: HPA Dataset Benchmark Scores
Model
Task 1
Task 2
Task 3
MorphEm (ours)
91.31
68.55
43.13
OpenPhenom (ViT Small)
78.84
50.91
35.40
DINOv2 (VIT Small)
70.07
53.00
26.33
IDRCell (VIT Small)
70.86
49.90
30.90
Table F12: CP Dataset Benchmark Scores
Model
Task 1
Task 2
Task
Task 4
MorphFm (aura)
88.90
51.73
21.83
12.57
OpenPhenon Phemom (VIT Small)
88.09
44.23
23.73
18.26
DINOv2 (VIT Small)
88.21
49.75
21.97
11.88
IDRCell (VIT Small)
79.29
51.43
20.63
07.25
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F.1.1 TESTED CONFIGURATIONS FOR SUBCELL
Here, we showcase the number of SubCell configurations that were tested for the CHAMMI benchmark. Each row presents results with different models. The input channel config- urations of four models are bg, rgb, ybg, and rybg, where each letter defines the input channel. The general protein of interest (g) that localizes into different cellular compart- ments is mandatory imput to all models together with mucleus (b) reference channel. The other two reference channels, microtubules (r) and endoplasmic reticulum (y), are present two model configurations. The HPA benchmark uses the original channel configuration of the SubCell paper. For the WTC benchmark, the 'bg" model is run twice with different these extracted
imput channels ('g' as membrane or protein) followed by concatenation features. A similar approach is utilized for the CP benchmark, where three different inputs (Syto, WGA, and Mitotracker) are used for 'g' input channel. We have reported all the configurations tested for all the different models types for WTC, HPA, and CP in F F13
F14 and F15 respectively.
Table F13: SubCell WTC Scores
Model
WTC
Task 1
Task 2
MAAE-CellS-PPooS-PPoa bg
55.06
78.22
55.06 33.70
ViT-Prrot-PPol bg
33.70
57.51
Table F14: SubCell HPA Scores
Model
HPA
Task 1
Task
Task 3
MAE-CelIS-PD (Ceell--ProS--- PwitPPPooPooo rybg
64.58
96.26
84.01
45.15
MAF-CellS-Protts PicellsSPrrtS-Poood rbg
71.63
95.59
88.07
55.19
MANE-CelSS-ProtS-Pee ybg
66.93
95.03
79.73
54.12
MAE-CelIS-PPe Prciii--rrttSPoooo bg
65.63
93.40
79.85
51.41
VrT-ProtS-PP Pooll rybg
76.83
99.05
91.00
62.67
ViT-ProLS- rbg
76.52
97.89
88.93
64.11
ViT-ProtS-Pode ybg
74.48
97.73
83.03
65.92
ViT-ProtS- bg
72.96
96.06
82.70
63.23
Table F15:
SubCell CP Scores
Model
CP
Task 1
Task
Task 3
Task
MAF-CellS-ProlS-PPdd ybg
28.38
69.42
51.51 54.91
22.92
10.70
ViT-ProtSSPool ybg
28.25
87.47
19.23
10.59
F.2 HPAv23 AT 256x256
HPAv23 at 256x256 is version of the original SubCell training images where the resolution of the images has been reduced from 1024x1024 to 256x256 with a resize. This transfor- mation was done to reduce the compute time, and storage taken by the test set. We ran protein localization with HPAv23 at 256x256 in both of its configurations: challenge_ cats
in F16 and unique cats F17
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Table F16: Protein Localization (Challenge Classification Labels)
Model
Macro AP
Micro AP
MorphEm (ours)
58.87
80.47
OpenPhenom (ViT Small)
49.13
75.75
DINOv2 (VIT Small)
53.76
77.01
SubCell (VIT Base)
69.33
84.79
IDRCell (VIT Small)
44.05
72.86
Table F17: Protein Localization (Unique Classification Labels)
Model
Macro AP
Micro AP
MorphEm (ours)
44.38
78.07
OpenPhenom (VIT Small)
35.98
73.13
DINOv2 (VIT Small)
39.67
74.84
SubCell (ViT Base)
52.60
82.58
IDRCell (VIT Small)
44.05
72.86
F.3 JUMP-CP1 COMPOUNDS
Original JUMP-CP1 images published with normalized (median absolute deviation MAD-robustize) well-level (replicate) CellProfiler features. Those features also include the that were extracted from brightfield images, for fair comparison those features were excluded. We also processed raw features by ofurselves: the same features were selected as in the paper version (except for features from brightfield channels) and performed ZCA- whitening (spherize) with pyCytominer in a similar way as we did for deep learning fea- tures. We also report the results obtained with Cell Painting CNN (Moshkov et al. 2024). feature post-proccssing was the same as for other deep learning features. Alongside the motrics JUMP-CP1 and JUMP-CP2, we also report the Active fraction, that fraction of phenotypically active compounds versus negative controls and mAP-s from those active compounds contribute to JUMP-CP result (Kalinin el al. 2025). otherwise mAP for non- active compounds would be zero. We reported our numbers with active fraction in F18
Table F18: Phenotypic quality
Model
Active fraction
JUMP-CP1
JUMP-CP2
MorphEm (murs)
94.44%
76.32
06.79
CellProfiler (paper)
81.70%
58.71
04.00
CellProfiler (ours)
99.02%
74.12
03.61
Cell Painting CNN
98.04%
77.45
06.80
OpenPhenom (ViT Small)
96.08%
74.26
04.99
DINOv2 (VIT Small)
94.44%
75.84
07.03
SubCell (ViT Base)
95.42%
77.60
07.44
IDRCell (ViT Small)
93.46%
72.37
04.98
F.4 IDR-17
IDR-17 is benchmarking based on themical-genetee interaction map of small molecules using high-throughput imaging in cancer cells. Add all the eight different cell lines, and the composite scores
We are going to be reporting all three metrics we have: ROC AUC scores in F20 Recalló 100 in Table F19
F21
Recal Re.a
43

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Published as a conference paper at ICLR 2026
Table F19: Recall Scores with Different Models
Cell Line
DINOv2 SubCell OpenPhenom MorphEm (ours)
IDRCell
HCT116 02-006
54.81
54.80
52.88
53.85
49.04
HCT116 02-008
62.79
62.79
60.47
65.12
62.79
HCT116 02-030
27.12
27.12
32.20
28.81
27.12
HCT116 02-031
54.29
57.14
54.29
57.14
51.43
HCT116 104-001
25.40
26.98
25.40
23.81
26.98
HCT116 104-004
41.10
39.73
36.99
39.73
36.99
HCT116 104-007
32.91
32.91
30.38
32.91
32.91
HCT116 104-008
39.58
41.67
34.38
40.63
34.38
Average
42.24
42.89
40.87
42.75
40.20
Table F20: Recall@50 Scores for Different Models
Cell Line
DINOv2 SubCell OpenPhenom MorphEm (ours)
IDRCell
HCT116 02-006
29.80
28.85
28.85
29.81
28.85
HCT116 02-008
46.51
44.19
46.51
46.15
44.19
HCT116 02-030
15.25
15.25
15.25
15.25
15.25
HCT116 02-031
31.43
28.57
28.57
31.43
31.43
HCT116 104-001
17.46
15.87
17.46
15.87
15.87
HCT116 104-004
23.29
20.54
21.92
23.28
20.55
HCT116 104-007
20.25
20.25
20.25
20.25
21.52
HCT116 104-008
20.83
18.75
18.75
20.83
17.71
Average
25.60
24.04
24.70
25.40
24.42
Table F21: AUROC Scores for Different Models
Sample
DINOv2 SubCell OpenPhenom MorphEm (ours)
IDRCell
HCT116 02-006
82.13
82.75
81.77
83.74
81.08
HCT116 02-008
80.76
80.04
79.49
79.61
83.08
HCT116 02-030
67.30
67.10
67.32
66.28
65.74
HCT116 02-031
87.65
87.77
83.88
88.72
82.45
HCT116 104-001
65.07
64.44
63.24
65.28
61.97
HCT116 104-004
75.57
77.25
76.10
75.56
76.90
HCT116 104-007
67.43
66.35
73.06
68.12
72.40
HCT116 104-008
75.79
76.90
76.68
76.82
75.01
Average
75.21
75.37
75.19
75.52
74.83
44

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Published as a conference paper at ICLR 2026
F.5 CELLPHIE
Here, we report all the statistics for the CellPHIE benchmark in Table F22
Table F22: Comparison of Neuron-Features with Different Models
Neuton-Features
AUC
F1
Precision
Recall
Mo (ours)
80.51
77.45
79.91
75.54
CellProfiler
78.32
77.44
74.44
81.14
DINOv1 (Pretrained on NF)
73.72
72.30
74.76
70.44
OpenPhenom (VIT Small)
77.56
75.68
77.84
74.04
DINOv2 (ViT Small)
73.95
72.27
76.29
68.97
SubCell (ViT Base)
71.24
70.60
74.26
67.78
F.6 RBC-MC
Here, report all the statistics for the RBC-MC benchmark in Table F23
and Figure F2
Table F23: Comparision of RBC-MC with Different Models
Model
Accuracy on Swiss
Accuracy on Canadian Overall Accuracy
MorphEEn (ours)
68.34%
65.06%
66.70%
IDRCell
55.85%
51.18%
53.50%
DINOv2
59.41%
55.35%
57.40%
OpenPhenom
64.43%
61.05%
62.70%
SubCell
59.10%
53.50%
56.30%
MCA-SSL
59.50%
56.90%
58.20%
MCA-SupC
55.40%
51.20%
53.30%
ChA-MAEViT
61.5%
56.60%
59.00%
G EXPERIMENTS
G.1 MODEL CONFIGURATIONS AND PARAMETERS
All the models we trained have similar model configurations to have fair comparison.
G.1.1 MULTI-CHANNEL STRATEGIES
Multi-Channel Attention (MCA). Multi-channel attention models allow VIT net- work to compute attention across channels. These networks work by unraveling the channel dimension into the sequence input of the vision transformer and use learned channel em- beddings in order denote which tokens belong to which channels (similar to positional encodings). These embeddings are learned per channel type (e.g. DNA and RNA encod-
Vonsaare NWISS GRT (FCG
Voncate Canadion set
273
GE2
000
301
2.19
6.21
017
000
2.8
6.2
Shapk amedeco
0.9
684
515
008
305
6.6
637
SANDA SOAEEC
042
338
833
100
335
625
015
003
6.00
029
041
304
E71
0C4
364
2
00
Dos
Depp poodctee
Figure F22: Confusion Matrices for RBC-MC Classification
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Published as a conference paper at ICLR 2026
ings) and effect all tokens in given channel the same way. MCA was modified to use masked attention to handle mixed sequence lengths. Channels were padded with tokens if sequences were of smaller length and an attention bias was used for the softmax to ignore these tokens. Models were trained with a patch size or 16, for 100 epochs in total.
Bag of Channels (BoC). Bag of Channels models is strategy where we break all multi- channel images into single and the model Iearns from one single channel at a time. A drawback of this strategy is that the model does not learn inter-channel correlations it only learns from one channel at a time. During evaluation, we usually concatenate the feature enbedding space of all different test evaluation channels.
G.1.2 TYPES OF SSL MODELS
DINO. DINO is a self-supervised framework that employs student-teacher network to learn representations in imaging (Caron et al. 2021
MAE. MAF is self=supervised framework that masks random patches and reconstructs the missing pixels (He et al. 2022).
SimCLR. SimCLR is a self-supervised framework that uses data augmentations to perform contrastive learning with images. (Chen et al. 2020
G.1.3 MODEL PARAMETERS
The default training parameters of the DINO model were used except the learning rate changed lo 5e-5. Batch sizes varied according to model sizes with 256, 128, and 32 used for ViT Small, VIT Base, and VIT Large respectively. We the model with horizontal and vertical flips for augmentations.
DINO BoC.
MAE BoC. The default training parameters of the MAE model were used except- learning rate changed to 5e-4, weight decay changed to 0.04, warm-up epochs changed to 10, and the number of epochs changed to 100 epochs. Batch sizes varied according to model sizes with 1024, 768, and 384 used for ViT Small, ViT Basc, and ViT Large respectively. We ran the model with horizontal and vertical flips for augmentations.
SimCLR BoC. The default training parameters of the SimCLR model were used except learning rate changed to 5e-5, weight decay changed 0.04, warm-up epochs changed to 10, and the number of epochs changed to 100 epochs. The model was run with random resized crop with scale (0.2, I.0), RaandomlorizonnllFPii RandomVerticalFliig and Gaussian blurring to develop two samples.
Channel- VIT DINO. Channel-Vin VIT DINO replaces the fixed channel VIT backbone with MCA VIT backbone and uses the standard DINO SSL algorithm to optimize the network. The multi channel id metadata was used to gather multii-hannnl images. DINO augmenta- tions than with horizontal and vertical flips with 8 local crops and 2 global crops. DINO global crops were 224x224 and local crops were 96x96 with crop ratios of 0.4-1.0 and 0.05-0.4 respectively. Learning rate was warmed up from 1e-6 to to 0.0001 for all models over 10 epochs. Teacher temperature was warmed up from 0.04 to 0.07 over 30 epochs. Weight Decay was warmed up from 0.04 to 0.04 over 10 epochs. The AdamW optimizer was used with default parameters other than those discussed previously. We trained with standard cosine annealing schedules as defined in (Caron et al. 2021). MCA DINO was modified to use masked attention to handle mixed sequence lengths. Channels were padded with 0 tokens if sequences were of smaller length and an attention bias was used for the softmax to ignore these tokens. Models were trained with patch size or 16, for 100 epochs in total.
Channel- SimCLR. Channel-ViT SimCLR replaces the fixed channel ViT back- bone with an MCA ViT backbone and uses the standard SimCLR SSL algorithm. The multi channel id metadata was used to gather multi- images. The model was run with random resized crop with scale (0.2, 1.0), RandomHooiizotallFin RandomVerticalFliip. and Gaussian blurring to develop two samples. The learning rate used was 5e-5.
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Channel- VIT MAE. Channel-Vil MAE replaces the fixed channel ViT backbone with an MCA VIT backbone and uses the standard MAE SSL algorithm. The multi Saallti_hhanne_____ id metadata used to gather multi-channel images. The model zontalFlip, and RandonVerticalFlip.. The learning rate used was 5e-5.
with RandomHori-
G.1.4 SUPERVISED BASELINE
ViT-small models were trained with a fixed number of channels per dataset CHAMMT. A drop path rate of 0.2 was used for each block in the transformer. The Adamm optimizer was used with a learning rate of 0.001 and weight decay of 0.4. Augmentations of randomly resized crops, horizontal flips, rotations, gaussian blurring and self-normalization were used. A prediction head was used to go from the CLS token to the number of classes each dataset. This
linear head with L1 and L2 norms applied, with lambdas of 0.01. A cosign annealing along the learning rate was applied. No warmup was used. Standard cross entropy loss used to train the network with 8 L40S GPUs for 60-100 epochs, depending on when scores stopped improving. The maximum score along the epochs was then used for the result of each CHAMMI dataset in the score G24
Table G24: Summary of IID Mcan, CHAMMI Score for Supervised Bascline)
Model
IID Mean
CHAMMI Score
WTC
HPA
CP
Supervised Baseline
78.76
54.56
65.21
71.84
26.64
G.2 RESOURCES USED
7-10 GPUs of NVidia A6000, L40S, and L40 were used with servers having 96 CPUs and server memory 512 GB.
G.3 SCALING COMPUTATIONS
Here, describe how the computation varies for Bag of Channels and Multi-Channel Attention models. Figure G23 showcases how much more compute time Multi-channel models take compared Bag of Channels models.
1200
Model_ Mamel_Name
DINO MAE Model_ Type
1000
3.7X
800
Multichannel
GPU Hours
400
5.8X
200
3ds
10ds
small
Dataset_Size
Figure G23: Compute time in GPU hours (vertical axis) of model training when the dataset size is increased (horizontal axis). The dataset sizes are 3ds (100K images), 10ds (150K images), and small (74ds with 560K imagos).
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G.4 ADDITIONAL EXPERIMENTS
G.4.1 DATASET SCALING WITH HPAv23 AT 256X256
Our evaluation mainly used CHAMMI an indicator of model performance in the scalability analysis where the number of sampled images used for training is increased. Table G25 reports the results of evaluating these models in the HPAv23 at 256x256 benchmark. The results show a trend similar to the CHAMMI score in Figure where performance improves with more data.
Table G25: DINO Dataset Scaling Using HPAw23 at 256x256.
Dataset Configuration
Macro AP
3 Datasets
41.00
10 Datasets
53.40
CHAMMI-75 Small
54.92
CHAMMI-75 Large
59.17
G.4.2 HI-RES VS LOW-RES HPA EXPERIMENTS
We adopted the HPAv23 dataset for benchmarking protein localization classification. This dataset contains single-cell images at high resolution (1024x1024), which has high compute cost at test time. To accelerate evaluations and faciliate comparisons across models, we reduced the size of the images 256x256 pixels, resulting in lower resolution.
Table reports the differences in performances between high resolution classification and low resolution with images at 256x256 pixels. The model used in this evaluation is our best DINO-BoC model trained with CHAMMI-75. The results indicate that using low resolution images results lower performance, as expected. However, the drop is very small; less than one point in the Kaggle Challenge task, and about 3 points in micro average precision for the unique categories task. The largest drop observed in macro average precision for the unique categories task, which reveals that some of the classes with rare examples are missed at low resolution.
We conclude that testing at low resolution is still meaningful, while allowing for faster evaluation. The evaluation time is reduced from approx. hour at high resolution to less than 10 minutes at low resolution, both using eight NVidia GPUs.
Table G26: Comparison Metrics for Hi-Res (1024x1024 pixels) vs Low-Res (256x256 pixels).
Metric
Task
Hi-Res
Low-Res
Drop
Macro AP
Kaggle Challenge (19 categories)
59.59
58.87
-0.72
Micro AP
Kaggle Challenge (19 categories)
80.84
80.47
-0.37
Macro AP
Unique (31 categories)
59.59
44.38
-15.21
Micro AP
Unique (31 categories)
80.84
78.07
-2.77
G.5 FINE-TUNING MODELS ON DOWNSTREAM TASKS
While experiments successfully utilize the pre-trained CHAMMI-75 model with only linear probe (frozen backbone) to achieve state-of-the-art results, researchers may explore fine-tuning for specific, complex downstream tasks. However, based on our experience, caution that full fine-tuning of multi-channel models for cell phenolyping is oflen challeng- ing due the scarcity of large, high-quality supervised labels, leading frequently to poor generalization and rapid overfitting.
When to avoid fine-tuning (default recommendation). We strongly recommend avoiding fine-tuning when the downstream task relies subtle phenotypic differences with- out clean, manually validated labels (e.g., weak treatment labels small screens). Also when the available labeled data. is small, as ViT model (even small size) tends to quickly
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overfit the batch-specific signal in the training set. In such cases, extracting features with a frozen CHAMMI-75 backbone and training a simple linear classifier (as done in our HPAv23 and RBC-MC benchmarks) is the most robust and computationally efficient approach.
Best practices for finc-tuning (only if necessary). If a rescarcher must finc-tune the backbone (e.g., if highly clean, validated annotation sets like those in HPAv23 avail- able), they should follow these steps: (1) Input Standardization: the input images must follow the same preprocessing steps applied during CHAMMI-75 training, which include in- tensity normalization by applying 0.1% and 99.9% percentile clipping per channel, followed by per-channel self-normalization. (2) Spatial cropping: use single-cell coordinates to ensure crops are centered on relevant cellular content. Architecture adaptation (bag-of-channels): for a target task with N channels, adapt the BoC architecture by fixing N. This involves replicating the pre-trained weights of the first layer (the convolution patch embedding layer) N times to accept the N-channel input image. The remainder of the model architec- ture (the transformer blocks) remains unchanged and loaded with the pre-trained weights. (3) Training protocol: employ conservative training schedules with a very low learning rate (e.g., 10-3 to 10-6) for the feature extractor backbone. Use strong regularization (e.g.> high weight decay) to prevent catastrophic forgetting of the general knowledge learned from CHAMMI-75
We reiterate that none of the results reported in this paper were obtained through fine- tuning; instead, found that linear probing yielded the most stable and generalizable performance across all phenotyping tasks.
49


Reviews:
[{"review_id": "tRWeQKaHOP", "reviewer": "Reviewer_T98S", "summary": "The paper presents CHAMMI-75, a curated dataset of multi-channel microscopy images from 75 studies with standardized formats, and rich metadata for content-aware sampling. The authors focus on data acquisition, metadata integration, and redundancy reduction, then use the dataset to pre-train ViT models. Benchmarks span five tasks, including two newly constructed evaluations. A DINO-based BoC ViT-small pretrained on CHAMMI-75 delivers the strongest SSL results across most tasks, shows favorable data/model scaling trends, and outperforms models trained on narrower sources.", "questions": "Please refer to my weakness section.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- The dataset has unmatched scale and heterogeneity for multi-channel microscopy, which supports broad generalization.\n- The benchmarks cover diverse, realistic tasks and include novel channel combinations, which enhances external validity.\n- The scaling analysis is careful and practical, which offers guidance on data size, model size, and multi-channel strategy.\n- The SSL results are consistently strong across tasks, which validates CHAMMI-75 as a useful pre-training resource. And the release plan supports reproducibility and downstream use.", "weaknesses": "- The work offers limited methodological novelty, which centers contributions on data and benchmarking.\n- The comparison to SubCell mixes training regimes and model sizes, which hinders clean conclusions about capability gaps.\n- The LLM-assisted metadata extraction lacks error quantification, which raises concerns about label noise in curation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wJB0VUFMWj", "reviewer": "Reviewer_6K5j", "summary": "The paper presents CHAMMI-75, a dataset of heterogeneous multi-channel microscopy images for pre-training channel-adaptive cellular image analysis models. The dataset comprises 2.8 million multi-channel images from 75 diverse sources, featuring more than 1.8 billion cells, resulting in a curated, high-quality resource that exhibits greater biological and technical variation than other microscopy datasets used in prior work. The authors train vision transformer models using self-supervised methods, such as DINO, MAE, and SimCLR. The trained models are then evaluated on several downstream tasks, like cell-cycle stage classification and protein localization classification, among others.", "questions": "* How does CHAMMI-75 handle cases where biological metadata (e.g., organism or stain identity) is ambiguous or inconsistent? \n\n* Did the authors attempt cross-domain zero-shot evaluations (e.g., predicting unseen channel types)?\n\n* How do the learned embeddings compare qualitatively (e.g., via t-SNE or UMAP) across datasets with different channel counts?\nTo better support the claim that CHAMMI-75 enables models to learn domain-invariant and biologically meaningful representations, I recommend including qualitative embedding visualizations such as UMAPs or t-SNE plots of the learned single-cell features. These could illustrate how samples cluster across different datasets, channel configurations, or biological conditions (e.g., cell type, treatment, protein localization). Comparing embeddings from representative models would provide intuitive evidence for cross-domain consistency and morphology awareness, complementing the quantitative benchmarks\n\n* Can authors provide guidance for researchers who want to finetune models on their own microscopy data (e.g., normalization or channel alignment practices)?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "The dataset’s large scale is impressive and would be a great unified resource for other researchers. The results for downstream tasks provide real value. The authors also added metadata information, specifically creating 22 metadata fields. Additionally, they segmented the nucleus of 1.8 billion cells using CellPose, which is very useful for the community. The benchmarks across different microscopy tasks demonstrate the generalizability of the proposed models. The statement from the authors to make the code and the dataset publicly available is highly appreciated and will help the community.", "weaknesses": "* While multiple benchmarks are used, the analysis could be strengthened by deeper biological interpretation of learned representations e.g., probing whether CHAMMI-75 pre-training improves biological feature disentanglement or transfer to unseen modalities\n\n* The paper acknowledges missing and inconsistent metadata, but the implications for training robustness and domain bias are missing/underexplored.\n\n* The evaluation seems to be focusing primarily on fluorescence microscopy. Brightfield datasets like LIVECell and EVICAN are part of the training set but aren’t used as an evaluation benchmark. Including label-free imaging in the evaluation would better display the transfer of learned representations across different modalities.\nThere are other recent papers like Segment Anything for Microscopy (Nature Methods, 2025), which discuss the cross-modality generalization in microscopy using foundation models for the task of segmentation. A clarification on how CHAMMI-75 complements or differs from such methods would highlight the contribution of this paper much more.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AC1qw3Ef2q", "reviewer": "Reviewer_q2zP", "summary": "This paper presents CHAMMI-75, a large-scale multi-channel microscopy dataset that aggregates 75 public biological studies and 18 imaging platforms. The authors provide detailed documentation of data collection, cleaning, redundancy control, and scaling studies comparing SSL frameworks on this dataset.\nWhile CHAMMI-75 fills an important data gap and is technically well executed, the paper focuses primarily on dataset construction and benchmarking. It lacks methodological innovations addressing the intrinsic challenge of multi-channel adaptation and provides limited analysis on how dataset diversity affects model generalization.", "questions": "The questions can be found in the weakness part. I understand this is a dataset and benchmark paper so the method part is not the research focus. However, additional validation experiments are essential for better understanding the benchmark. I am open to raise my score if more analysis is included in the rebuttal.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The CHAMMI-75 dataset integrates 75 biological projects and 18 imaging platforms, providing unprecedented scale and diversity for microscopy SSL. This makes it a valuable benchmark for future research.\n2. The authors conduct careful cleaning, redundancy reduction, and balanced sampling, improving representativeness and reproducibility.\n3. The paper includes a thorough comparison of SSL methods and analyzes performance scaling with dataset size and channel configurations, validating the dataset’s utility.", "weaknesses": "1. The paper’s main contribution lies in data curation and benchmarking. The experimental pipeline relies heavily on existing models without proposing new architectures or training strategies tailored to multi-channel adaptation.\n2. Although the dataset is multi-source and multi-channel, the study does not analyze how factors such as number of channels, organism diversity, or imaging modality affect representation learning. This limits understanding of what drives cross-domain generalization.\n3. The reported SSL benchmarks focus on general downstream accuracy rather than the core motivation of channel generalization or cross-modality transfer, leaving a gap between dataset design and evaluation goals.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)