Modeling the Density of Pixel-level Self-supervised Embeddings for Unsupervised Pathology Segmentation in Medical CT
Authors: Mikhail Goncharov
Eugenia Soboleva
Daniil Ignatyev
Mariia Donskova
Mikhail Belyaev
Ivan Oseledets
Marina Munkhoeva
Maxim Panov
Venue: ICLR 2026 Poster (2026)
Keywords: Unsupervised Visual Anomaly Segmentation
Self-supervised learning
Density estimation
Computed Tomography
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
TL;DR: We present Screener, a fully self-supervised pathology segmentation model for medical CT images, outperforming existing methods in both unsupervised and supervised fine-tuning settings.

Abstract: Accurate detection of all pathological findings in 3D medical images remains a significant challenge, as supervised models are limited to detecting only the few pathology classes annotated in existing datasets. To address this, we frame pathology detection as an unsupervised visual anomaly segmentation (UVAS) problem, leveraging the inherent rarity of pathological patterns compared to healthy ones. We enhance the existing density-based UVAS framework with two key innovations: (1) dense self-supervised learning for feature extraction, eliminating the need for supervised pretraining, and (2) learned, masking-invariant dense features as conditioning variables, replacing hand-crafted positional encodings. Trained on over 30,000 unlabeled 3D CT volumes, our fully self-supervised model, Screener, outperforms existing UVAS methods on four large-scale test datasets comprising 1,820 scans with diverse pathologies. Furthermore, in a low-shot supervised fine-tuning setting, Screener surpasses existing self-supervised pretraining methods, establishing it as a state-of-the-art foundation for pathology segmentation. The code and pretrained models are available at https://github.com/mishgon/screener.

Published as a conference paper at ICLR 2026
MODELING THE DENSITY OF PIXEL-LEVEL SELF-SUPERVISH EMBEDDINGS FOR UNSUPERVISED PATHOLOGY SEGMENTATION IN MEDICAL CT
Mikhail Goncharov 1.5.1
Eugenia Soboleva
Daniil Ignatyev
Mariia Donskova
Mikhail Belyaev
Ivan Oseledets'
Marina Munkhoeva'
Maxim Panoy
AXXX, Moscow, Russia
Yandex
AUMI AI
Lomonosov Moscow State University
MBZUAI
Pneumothorax, unannotated in the ground truth mask
But detected by Screener
Figure 1: Examples of CT image slices (the first row), the ground truth pathology masks (the second row) and the anomaly maps predicted by our fully self-supervised Screener model (the third row).
ABSTRACT
Accurate detection of all pathological findings in 3D medical images remains a significant challenge, as supervised models are limited to detecting only the few pathology classes annotated existing datasets. To address this, we frame pathology detection as an unsupervised visual anomaly segmentation (UVAS) problem, leveraging the inherent rarity of pathological patterns compared healthy ones. We enhance the existing density- -based UVAS framework with two key innovations: (1) dense self-supervised learning for feature extraction, elim- inating the need for supervised pretraining, and (2) learned, masking- masking-invariant invariant dense features as conditioning variables, replacing hand-crafted positional en- codings. Trained over 30,000 unlabeled 3D CT volumes, our fully self- supervised model, Screener, outperforms existing UVAS methods on four large- scale test datasets comprising 1,820 scans with diverse pathologies. Further- more, in a low-shot supervised fine-tuning setting, Screener surpasses existing self-superviser pretraining methods, establishing it tate-of-thh-ar founda- tion for pathology segmentation. The code and pretrained models are available at nttps://githus wtt.://..ooooommoooo...oooooccot wwwpseegggggeeeerrrreeeeeeee
INTRODUCTION
Accurate identification, localization, and classification of all pathological findings in 3D medical images remain significant challenge in medical computer vision. While supervised models have
"Equal contribution.
Corresponding author: mi

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shown promise, their utiliity is limited by the scarcity of labeled datasets, which often contain annota- tions for only a few pathologies. For example, Jigure[]shows 2D slices of 3D computed tomography (CT) images (first row) from public datasets providing annotations of lung cancer, pneumonia, kid- ney tumors, or liver tumors, while annotations of other pathologies, e.g., pneumothorax, are missing. This restricts supervised models to narrow, task-specific applications.
However, unlabeled CT datasets are abundant but often remain unused. Leveraging these datasets, aim to develop an unsupervised model capable of distinguishing pathological regions from nor- mal ones. Our core assumption is that pathological patterns are statistically rarer than healthy pat- terns in CT images. This frames pathology segmentation as an unsupervised visual anomaly seg- mentation (UVAS) problem.
Although existing UVAS methods have been extensively explored for natural images, their adapta- tion to medical imaging is challenging. One obstacle is that uncurated CT datasets include many patients with pathologies, and there is no automatic way filter them out to ensure a training set composed entirely of normal (healthy) images a common requirement for synthetic-based (Zavr- tanik et al. 2021; Marimont & Tarroni 2023) and reconstruction-bant
(Baur et al. 2021 Schlegl et al. 2019) UVAS methods. Density-based approaches (Gudovskiy et al. (002 Zhou et al. 2024) are better suited, as they model image patterns probabilisticall and assume abnormal patterns are rare rather than absent. To model the density of image patterns, existing methods encode them into feature maps using an ImageNet-pretraine, encoder. Therefore, their performance on medical im- ages degrades due domain shift. Supervised medical encoders like STU-Net (Huang al. 2023) might seem viable, but our experiments show they also underperform, likely because their features are too specific and lack discriminative information for pathology segmentation.
To address these challenges, we propose using dense self-supervised learning (SSL) (O. Pinheiro al., 2020) to pretrain more discriminative feature maps of CT images and employ them in the UVAS framework. Thus, our model Iearns the distribution of dense SSL embeddings and assigns high anomaly scores image regions where embeddings fall into low-density regions.
Inspired by dense SSL, we also generalize the idea of conditioning in density-based UVAS methods. Existing works (Gudovskiy et al. 2022 Zhou et al. 2024) use caad-erafted conditioning variables such as pixel-wise sinusoidal positional embeddings. We replace them by learned pixel-wise contex- tual embeddings capturing global characteristics individual image regions, e.g. their anatomical position, patient's s age, etc. At the same time, we eliminate local information about presence of pathologies from the learned conditioning variables by enforcing their invariance to image masking.
We train our model, Screener, 30,000 unlabeled CT volumes and evaluate it on 1,820 scans in two settings. First, as a fully unsupervised model, achieves remarkable results (Figure[p, [.. signifi- cantly outperforming existing UVAS methods. Second, after fine-tuning for downstream pathology segmentation tasks, Screener rivals other state-of-the-ar pretrained models.
Our key contributions are four-fold:
Dense self-supervised features for beased UVAS. We demonstrate that dense self- supervised representations can be successfully used and even preferred over supervised fea- ture extractors density-based UVAS methods. This enables a novel fully self-supervised UVAS framework for domains with limited labeled data.
Learned conditioning variables. We propose novel self-supervised conditioning variables for density-based UVAS. simplifying the conditional distributions and enabling a simple Gaussian density model to perform on par with normalizing flows.
State-of-the-art UVAS results in CT. This work presents the first large-scale evaluation of UVAS methods for CT images, showing state-of-the-art performance on unsupervised semantic segmentation of pathologies in diverse anatomical regions, including lung cancer, pneumonia, liver and kidney tumors.
State-of-the-art pretraining for pathology segmentation. We introduce a novel pretrain- ing method that distills Screener into a UNet, enabling supervised fine-tuning and matching the performance of tfth--f self-supervised pretraining methods.
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2 BACKGROUND & NOTATION
2.1 DENSITY-BASED UVAS
The core idea of density- based UVAS methods is assign high anomaly scores to image regions containing statistically rare patterns. To implement this idea, they involve two models, which we call a descriptor model and a density model. The descriptor model encodes image patterns into vector representations, while the density model learns their distribution and assigns anomaly scores.
The descriptor model Sam is usually a pretrained fully-convolutional neural network. For 3D image x E R Hxxx5 xx it produces feature maps y E Bxx=xexd consisting of vectors ylp] E Rd* which call descriptors of positions p P = {1..... h} [1....,w} {1.....s).
The density model (y) estimates the descriptors" marginal density gy (here, denotes the descriptor of random position in random image). For abnormal pattern at position p. the deseriptor y ypp is expected to lie in a low-density region, resulting in low gaan (y(p)) Conversely, normal patterns correspond to high density values. During inference, the negative log-density values,
- -log qo (y [p]), are used as anomaly segmentation scores.
This framework can be extended with a conditioning mechanism. For each position p, an auxiliary variable c[p] E R called a condition, introduced. Instead of modeling the marginal density Qr (v), the conditional density qy c(y c) is learned for each condition c, where (Y, C") represents the descriptor and condition at random position in a random image. At inference, the negative oog-conitionnl densities, llog qpam (y[p) c(pl), serve anomaly scores. state-of-the-an meth- ods (Gudovskiy et al.) 2022] Zhou et al. 2024 follow this conditional framework using sinusoidal positional encodings as conditions.
2.2 DENSE JOINT EMBEDDING SSL
Joint embedding SSL models learn meaningful image embeddings by generating positive pairs augmented views the same image (e.g.. random crops). They optimize embeddings to capture mutual information between views, making them both discriminative (distinguishing images) and augmentation-invarie (predictable across views). Contrastive methods, e.g. SimCLR (Chen et al.) 2020), explicitly push apart embeddings of different images, while non-contrastive methods, e.g.. VICReg (Bardes et al., 2021). avoid embeddings' collapse through regularization. Details on Sim- CLR and VICReg are the Appendix B
Dense SSL methods extend this idea to learn image feature maps consisting of pixel-wise embed- dings that encode information about different spatial positions in the image. To this end, they define positive pairs at the pixel level: two embeddings are positive if they correspond to the same absolute position in the original image, but are predicted from different augmented crops (see the upper part of Figure 2 for illustration). Thus, dense SSL enforces feature maps to be equivariant w.r.t. crops, while encouraging dissimilarity between embeddings from different positions. DenseCL (Wang et al. 2021 and VADER (O. Pinheiro et al. 2020) contrastive losses, while VICRegL (Bardes ct al. 2022 adopts a VICReg objective.
3 METHOD
Novelty statement. Our method, illustrated in Figure enhances the density-based UVAS frame- work with two key innovations. First, instead of relying generic backbones, we pretrain our descriptor model via dense SSL which enables domain-specific, high-resolution, customizable and more discriminative descriptors (Section 3.1. Second, we introduce novel maaking--nnvriicn con- ditioning variables, also learned via dense SSL (Section B.2). and largely simplifying further con- ditional density modeling (Section 3.3). Beyond these contributions, inference pipeline to single UNet architecture, which makes suitable for further supervised fine- tuning. This allows to reinterpret framework novel self- superviedd pretraining method.
distill the overall UVAS
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Descriptor model training
Xx H FxIxxxxxx
FxdxRxuXI
Condition model training
Iesond
Iecoad
Density model training
Ogikas
c/pl
9 Agrand
log (y)=)) <fpl)
Figure 2: Illustration of Screener. First, we pretrain descriptor model to produce discriminative feature maps, equivariant w.r.t. image crops and rescaling and invariant w.r.t. color jitter. Second, we train a condition model in the same way as the descriptor model, but also enforcing invariance image masking. Third, a density model learns the conditional distribution (ylc))))) of feature vectors Y vpp] and C clp) extracted by the descriptor and condition models from random image at random position p. To obtain anomaly maps we apply the density model in a pixel-wise convolutions.
manner, which can be efficiently implemented using
3.1 DESCRIPTOR MODEL
The success of our method relies high-quality descriptors that are discriminative of pathology yet robust to irrelevant normal variations. Dense SSL provides principled way to achieve this balance: voxel-level objectives encourage spatial discrimination, while augmentation invariance eliminates low-level details, leading to smoother, more semantically structured embedding space in which similar normal patterns map to high-density areas.
Our descriptor model design follows domain-driven minimalistic principles, differing from the prior dense SSL literature (Wang et al., 2021] [ardes et al. (002)). We adopt a UNet-like architecture, which has proven strong dense feature extractor in 3D medical imaging. Full resolution output enables precise localization of small pathologies. Each training batch includes embeddings from nearby voxels, forcing distinction of even spatially adjacent locations. We omit auxiliary global objectives or multi-scale feature pyramids our approach is simple and principled, relying solely on dense seff-supervision at full resolution.
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The training process is illustrated in the upper part of Figure 2 From random CT volume x, extract two overlapping, randomly sized 3D crops, resize them to H x W x S. and apply augmen- tations such as color jitter. The augmented crops, denoted x(1) and x(2), are passed through the descriptor model to produce feature maps y(1) and y(2). From the overlapping region of the two crops, we randomly select n positions. For each position p. we compute its coordinates p(1) and p( relative to the augmented views, and extract descriptors y (p)) and y [p These descriptors form a positive pair, as they correspond to the same position in the original vol- ume but are predicted from different augmentations. Repeating this process for m different seed
CT volumes yields a batch of N = n m positive pairs, denoted {(,, (2) y/
22) ) These em-
beddings are then optimized using standard dense SSL objectives, such as InfoNCE (Chen et al.
2020) or VICReg (Bardes et al., (021], described in Appendix B We refer to the resulting models as DenseInfoNCE and Dense respectively.
3.2 CONDITION MODEL
In medical imaging, the statistical plausibility of local pattern often depends on its broader context, such anatomical location or patient characteristics. This motivates modeling the conditional dis- tribution of descriptors, given relevant contextual variables. Conditioning offers two key ad advantage:: it simplifies density estimation, as conditional distributions are usually less complex than marginal, and it may lead to more semantically meaningful anomalies, defined as deviations from what is ex- pected in specific context. For example, a pattern normal in one anatomical region or patient group (e.g., calcification in an elderly lung) might be abnormal in another (e.g., calcification in breast).
Conditioning variables can be global (e.g., patient metadata like age or sex) or voxel-wise, en- abling region-specific conditioning. Gemeral-domain UVAS methods (Gudovskiy et al., 2022, ZZou (et al. 2024) utilize sinusoidal positional encodings of absolute spatial coordinates relative to the image origin. However, since medical scans may not be anatomically aligned, vanilla positional encodings lack consistent anatomical or patient-specific relevance. Anatomical Positional Embed- dings (APE) (Goncharov et al.] (2024) offer an alternative by encoding pixels' anatomical locations (though previously used for retrieval, not UVAS conditioning). However, it is domain-specific and may not capture all patient-level or fine-grained contextual nuances.
To address the aforementioned limitations, we propose a donain-agnostic self-supervised method for learning conditions. Our key idea is to train condition model go predict voxel-wise embeddings that are consistent across different masked image views. For instance, as illustrated in Figure the model learns predict the same condition embedding for a location even if a pathology is visible in one masked view but not another. Consequently, the learned condition feature maps become invariant the presence absence of anomalies. At the same time, we encourage intra- subject, i.e. spatial, and inter-subject discriminativeness and expect feature maps to capture voxel- level features such as anatomical location and tissue type, and patient-level characteristics such as age sex, which are robustly inferable from the global image structure. We empirically probe pathology-relan ignorance and anatomical awareness of our condition model in Appendix (D) The architecture and training procedure for the condition model grewd are exactly the same those for the descriptor model, with the sole difference: random masking as an additional augmentation.
3.3 DENSITY MODEL.
The conditional density model quin c) can be viewed as a predictive model, which tries to predict descriptors based on the corresponding conditions. In this interpretation, anomaly scores {-log Qoden (y [p] cyp)))pp are position-wise prediction errors. Also note, that marginal density model Qoten (1) is a special case of conditional model with a constant condition clp] = const.
During training, different CT images. For each crop, the pretrained descriptor and condition models produce the de- scriptor maps, {y and condition maps, {c: 1 and negative log- likelihodo loss is optimized:
sample a batch of random crops, {x; each of size H W x S, from
min Parm
log Qoren (y:(p) c; (p)p))
m
T= P
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At inference, we divide an imput CT image into M overlapping patches, {x)m each of size H x W S. For each patch, we apply the descriptor, condition. and density models to compute the anomaly map, {- log 98am (y:[pl c:(p))) These patch-wise anomaly maps then aggregated into a single anomaly map aligned with the entire input volume. During aggregation, we average the predictions in patches' overlapping regions.
We explore two parameterizations for the density model goten (y c): Gaussian, as a straightforward baseline, and normalizing flows, similar Gudovvskiy al. (2022): Zhou et al. (2024), as an expres- sive generative model enabling tractable density estimation. These parameterizations and the details of their implementation in the context of UVAS framework are further described in Appendix
3.4 DISTILLATION AND SUPERVISED FINE-TUNING
Although unsupervised Screener shows impressive results, supervised fine-tuning is the most prac- tical way to further improve its performance. The density-based UVAS pipeline, consisting of three separate models, is amenable to end-to-end optimization. To enable fine-tuning, distill the knowledge from the pretrained Screener into single UNet architecture. This step can be viewed as a novel self-supervised pretraining method for pathology segmentation tasks.
During distillation, we sample random image crops, pass them through the pretrained modular Screener to obtain ground truth anomaly score maps (negative log-density values). We then train regression UNet model (last conv has one output channel without activation) to predict these score maps directly from the input image crops using a simple MSE loss. For supervised fine-tuning on binary segmentation tasks, we randomly reinitialize the UNet's last conv layer and append a sig- moid activation. Then we fine-tune the model on task-specific labeled data using a combination of voxel-wise binary cross-entropy and Dice losses.
4 EXPERIMENTS
Our experiments can be divided into three main parts:
Unsupervised setting. We show that our unsupervised Screener significantly outperforms other UVAS methods on real-word medical CT datasets (Section 4.1).
Fine-tuning setting. We demonstrate that Screener can serve as state-of-the-art self- supervised pretraining method. To this end, we fine-tune the distilled Screener (as de- scribed in Section 3.4 for pathology segmentation tasks and compare it with supervised model trained from scratch, as well other fine-tuned pretrained models (Section 4.2)
Ablation study. We explore how different choices of descriptor, condition and density models in our method affect the UVAS results (Section 4.3
Datasets. We train Screener and other unsupervised models on three CT datasets: NLST TTam 2011]. AMOS (j et al., 2022), and AbdomenAtlas (Qu et al., 2024). These large-scale datasets include diverse patients with potential pathologies, but their annotations are not available for data filtering or training. For evaluation we use four datasets: LIDC (Armato III et al. 2011). MIDRC- RICORD- (Tsai et al.) 2020). KiTS (Heller al.] 2019) and LiTS (Bilic et al. 2023). These datasets provide annotation masks only for certain pathologies. Any other pathologies present in these datasets are not labeled. Summary table about the datasets is provided in Appendix
4.1 UNSUPERVISED SETTING
Evaluation protocol. We compare Screener with baseline UVAS models using voxel-level AU- ROC and Dice score. The segmentation threshold is selected to maximize Dice score on subset of 10 pathological cases, and then kept fixed for the entire test set. Note that Dice scores are sig- nificantly underestimated due to incomplete ground truth masks: while UVAS models aim to detect all anomalies, the datasets provide annotations only for specific target pathologies. Detections cor- responding to other unlabeled pathologies (see Figure [ and Appendix A are therefore mistakenly counted as false positives. Voxel-level AUROC is a standard UVAS metric because its estimation is more robust to the ground truth incompleteness issue. We estimate AUROC across all dataset

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voxels by sampling 1000 pathological voxels (contributing to true positive rate) and 1000 out-of- mask "normal" voxels (for false positive rate) per test image. The sampled "normal" voxels are overwhelmingly normal, ensuring accurate AUROC estimation despite incomplete annotations.
Results. Quantitative results are presented in Table [ Qualitative results are shown in Figure Screener significantly outperforms the UVAS baselines. Autoencoder, f-AnoGAN and Patched Diffusion Model tend to overfit to pathologies in the training data, and fail to reconstruct fine- grained normal details (see also Appendix II. Synthetics-based DRAEM and MOOD-Top! struggle to generalize to the appearance real medical pathologies. The density- MSFlow, relying on ImageNet-pretrainee features, proves ineffective at discriminating pathologies from normal regions in CT images.
Table 1: Comparison of Screener and the existing UVAS methods in unsupervised setting.
Model
Voxel-love AUROC
Dioe soxe
LITS
LITS
Autoencoder Bour et
2021
0.71
0.65
0.06
0.68
0.00 :: 0.00
0.00 =: 0.07
0.01 0:022
0.01 = 0.01
F-AnoGAN Schreg et ar. 2019
0.82
0.66
0.67
0.67
0.00 1: 0.00
0.09 2 0.07
0.01 0:0.2
0.01 2: 0.01
Parched Difratee
Benreedt 2004
0.87
0.776
0.76
0.80
0.01 0.03
0.14 0.08
0002-40..00 0.03
0.02 0.04
MOOD Tool Marrmmnt Tarroni 2023
0.79
0.79
0.77
0.80
0.00 1: 0.01
0.13 a: 0.10
0,02 0:07
0.06 2: 0.12
MSFlow Zholl a1 2014
0.71
0.67
0.63
0.63
0.00 + 0.01
0.08 = 0.06
0.01 0.00
0.00 =0 0.01
Screener
0.96
0.87
0.90
0.93
0.05
0.30 0.18
0.06 0.09
0.10
0.12
Patched Diffusion Madel
Input image
Autoencoder
F-AnOGAN
DRAEM
MOOD Top1
MSFlow
Screener (ours]
Ground truth
Figure 3: Qualitative comparison of anomaly maps produced by baseline UVAS methods and un- supervised Screener. First column contains CT slices, columns 2 to are the baseline methods' predictions, column the Screener's prediction. Last column depicts the ground truth mask.
4.2 FINE-TUNING SETTING
Evaluation protocol. We fine-tune and test pretrained models the evaluation datasets via 3-fold cross-validation. For each training fold, we use only 25 labeled cases, to amplify pretraining benefits and to conserve computational resources. Full-data fine-tuning results can be found in Appendix P] We assess the models using Dice score. We use a Wilcoxon signed-rank test to compare all the fine-tuned models with the nnUNet (Isensee al. 2021) trained from scratch.
Results. Results Table 2 demonstrate that Screener-based pretraining consistently improves
downstream segmentation performance across all test datasets in low-data fine-tuning setup, with significant gains on LIDC (a 1.5-fold Dice increase) and LiTS. Screener is competitive with super- vised pretraining (Huang et al. 2023) and state-of-the-art self-supervised VoCo (Wu et al. 2024).
and outperform other SSL models (Zhou et al. 2021 Tang et al. 2022 Valanarasu et al. 2023
Note that Dice scores are often underestimated in the unsupervised setting, as ground truth masks cover only certain target pathologies, while UVAS models intentionally detect all pathologies. Many true positives thus mistakenly counted false positives (see Figure and Appendix for examples).

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However, when fine-tuned on full datasets, Screener does not yield any gains compared to train- ing from scratch (see Appendix D. which is consistent with the broader observation that many SSL methods provide their strongest advantages when training data are scarce.
Table 2:
Dice scores of Screener and other self-supervised pretrained models after fine-tuning. We highlight statistically significant improvements (green) or declines (red) relative to nn trained from random initialization. Note that Grounding DINO (Liu et al., 2023) and LVM- Med (MH Nguyen et al., 2023) operate on 2D slices without access to full 3D image context, and therefore show inferior performance to other baselines which use 3D convolutions or 3D attention.
Model
LIDC
MIDRC
KITS
LiTS
nntiNer (random init.) Ssensee ot 2021
0.21
0.61
0.41
0.46
IntiNer (oupervised pretrain.) ) [Hoing. of 2023
0,20 1 40% <x<x 0.011
0.62 1 2X (p 0.51]
0.46 1 10%5 (r 0.01)
0.48 7% ( 0.01)
Grounding DINO Liu et all 2023
0.09 1 50% (p 0.01)
0.58 3% (p (.01)
0.19 L 53% (o (.01)
0.33 | 26% (p 0.01)
IVM-Med MH Neaven
2003
0.04 [ 80% 0.01)
0.55 5%
(p 0.01)
0.16 62% (p 0.01)
0:33 1 26% (p 0.00)
Model
1:1 (1.76)
0139 2%%
(..65)
0.31 L 18%
0.01)
0.39 1 12%
0.01)
SwiT
24% (p
0.55 9% (j 0.01)
53% [ 0.01)
0.39
(p 0.01]
DAE Valanar
96% 0.0)
0.58 45% (j 0.01)
0.26 38% [+ 0.01)
0.36
(p 0.01]
VcCo Wireeum 2014
0:30 ip= 0.70)
0.61 f (p= 0.89)
0.49 1 17%
0.01)
0.49 10% (p 0.01
MAE Waldetea 2025
0.24 [ 18% 0.01)
0.62 1 3% (p 0.01)
0.35 17% [p 0.04)
0.49 10% (p 0.01)
Dense VIC
0.22 1 7% (p 0.15)
0.58 L. 6% (p 0.01)
0.51 L 26% (p 0.01)
1 (p 0.92)
Screener (ours)
0.31 49% (p 0.013
0.62 3% (p 0.45)
0.43 1 6% (p= 0.17)
0.48 73% (p 0.01)
4.3
ABLATION STUDY
Table presents the ablation study of our proposed condition model. We compare our condition model with two baselines: vanilla sinusoidal positional encodings and APE (Goncharov et al.. (2024) detailed in Appendix C
We evaluate condition models in combination with the fixed Dense VICReg descriptor model and two different density models Gaussian and normalizing flow described in Appendix[E [ When we use expressive normalizing flow density model, all conditioning strategies yield results comparable to cach other and to the unconditional model. However, in experiments with simple Gaussian density models, we see that the results significantly improve as the condition- ing variables becomes more informative. Remarkably, our proposed masking- -ivvrriat condition model allows Gaussian model to achieve very strong anomaly segmentation results competing with complex flow-based models.
Table 3: Ablation study of the effect of conditional model for gaussian and flow-based density models. None in Condition model column means that results are given for marginal model.
Descripnce model
Condiiin model
Density model
Votci-level AUBOC
Dice score
LIDC
MIDRC
KiTS
LITS
LIIC
MIDRC
KITS
L:TS
DenseVICReg.Co
Nosi APE
Gaussian
0.51
0.61
0.61
0.71
0.00 + 0.00
0.17 0.11
0.00 0:00=0000
0.00 100-000
Sin
0.52
0.50
0.74
0.77
0.00 1 0,00
0.14
0.01 0.02
001 0.00
0.55
0.50
0.78
0.86
0.00 1
0.14:10:10
0.01 0:01:0001
001 1.000
Masking invariant
0.96
0.54
0.08
0.21 +: 0.18
0.00
0.13
Nene
Norm Bow
0.96
0.80
0.55
0.05
0.05 1: 0.12
0.51 0.118
0:0
0.0 0.112
0.96
0.50
0.05 0.13
0.30
0.06
0.10::0.12
APE
0.96
0.88
0.34
0.28 1.1.8
0.06 008
0.00 0.13
Masking-invariant
0.05 0.18
0.118
0.10 0.011
We also ablate different choices of descriptor model in Table 4 We compare DenseInfoNCE and Dense and conclude that dense VICReg objective works slightly better. We also compare two Dense VICReg models with different descriptors' dimensionality diese 32 or dd 128 and conclude that increasing dimensionality does not improve the results. To demonstrate the superior- ity of our domain-specific self-supervised descriptor model over supervised feature extractors, we compare them it with ImageNet-pretraaie ResNet50 (Zhou al. (024) and STU-Net (Huang et al., 2023
UNet pretrained in supervised manner on anatomical structure segmentation tasks.
RELATED WORK
Revonstruction-basee UVAS. Reconstruction- based methods train a generative model to recon- struct the original image from its compressed representation (Baur et al. 2021 Schlegl et al. 2019)
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Table 4: Ablation study of the effect of descriptor model. In these experiments we do use con- ditioning and use normalizing flow as a marginal density model. We include MSFlow (Zhou et al., 2024) to demonstrate that traageee--rretraiee descriptor model is inappropriate for 3D medical CT images.
Descrigtor model
Condition modell
Demsity model
Voxel-level AUROC
soum
LIDC
MIDRC
KITS
LTT
LJDC
MIDRC
KITS
LITS
ImageNet
cos pos.
MSflow
STU-NNt Huang
Neem.
0.52
0.64
0.64
-000000000
6.01
DenselnteNt
None
Norm
0.96
0.87
0.87
0.91
0.04
0.28 0.18
0.04 0.06
0.05
DanssViCReg. Dense VenseViCRos VICReg. ghec
Nose
Nerm. Blw
0.50
0.80
0.88
0.93
0.06 + 0.12
0.51= 0.11
0.04 = 0.06
0.00 # 0.12
None
Neem. flow
0.96
0.90
0.57
0.90
0.04 1 0.00
0.31 0.118
0.0.1:000 0.06
0,08 1.012
or from its corrupted, e.g., noised (Behrendt et al.) 2024), version. training set is anomaly-free these models struggle to reconstruct anomalies in the test set and absolute differences between the original and reconstructed pixel values can be used as anomaly maps. However, when training dataset contains real anomalies, reconstruction-basse models can learn reconstruct anomalies as well as normal regions, diminishing their ability to differentiate. Another limitation that measur- ing reconstruction errors in raw pixel space can be problematic: some abnormal pixels can acciden- taly have small reconstruction errors, while some normal fine-grained details, which are inherently difficult reconstruct precisely, might yield high reconstruction errors.
Synthetics-based UVAS. These methods rely on generating synthetic image anomalies and train- ing a supervised model to segment them. Anomalies can be simulated by corrupting random image regions with noise, replacing them with random patterns from specialized set (Zavrtanik et al.) 2021), or using parts of other training images (Marimont & Tarroni (023). While these models are straightforward to implement and train, they overfit to synthetic anomalies and struggle to generalize effectively to sal-world anomalies.
Densi UVAS. We explain the idea of density-based UVAS in Section 2.1 Some meth- ods (Roth et al. 2022) use non-parametric density models based on memory banks. More scalable flow-based methods (Yu et al., 2021 Gudovskiy al. [2022] Zhou al. 2024), leverage normaliz- ing flows. In our experiments, we included MSFlow (Zhou el al. (024). as it was among the top-5
performing methods on MVTecAD (Bergmann et al. 2021 at the time.
Medical UVAS. Recognized methods are either reconstruction beoonttruttoon-based (Baur al. 2021; Schlegl
et al. 2019 Pinaya et al. 2022 Behrendt et al., 2024) or synthetics-based (Marimont & Tarroni 2023 f-AnoGAN (Schlegl et al. 2019 trains generator g and discriminator to generate anomaly- free images x g(z) from latent variables 2. Then, it trains encoder to map anomaly-free images æ to the latent space, s.t. they can be reconstructed via frozen generator f = g g(f(x) se Patched Diffusion Model (Behrendt et al. 2024) cuts out image patches and trains diffusion model to reconstruct them based on the surrounding context. At inference, an image is split into a grid of patches and Diffusion model reconstructs each patch from its noised version based the remaining clean patches. MOOD-Topl (Marimont & Tarroni (2023) is a straightforward synthetics- based method showing top-1 performance on MOOD (Zimmerer et al. 2022).
Medical self-supervised pretraining. Methods like Model Genesis (Zhou al. 2021) and Swi- nUNETR (Tang et al. 2022) utilize combinations of contrastive learning, masked image modeling, and various pretext tasks re-implemented for 3D CT volumes. DAE (Valanarasu et al.) 2023) pre-
train a model to reconstruct original images from their disrupted versions created by local mask- ing across channel embeddings and low-level perturbations like noise and downsampling. Volume Contrast (VoCo) (Wu et al. 2024) employs contrastive approach to implicitly encode contextual position priors, treating different image regions as distinct "classes" and predicting which region a random sub-voluma belongs to by contrasting its representation against base crops. To our knowl- edge, Screener is the first work to propose and demonstrate the effectiveness of using unsupervised anomaly segmentation pretraining strategy for downstream pathology segmentation tasks.

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6 CONCLUSION
Our work addresses the critical challenge of detecting all pathological findings in 3D CT images, a task hindered by limited labeled data. Assuming the inherent rarity pathological patterns, frame this UVAS problem. We propose Screener, novel density-basee UVAS frame- work with dense SSL, ensuring discriminative and robust domain-specifie descriptors, and learned, masking-invariant conditioning variables that simplify density modeling. Evaluated on four large- scale datasets, the fully unsupervised Screener achieved state-of-the-art performance, effectively localizing diverse pathologies. Furthermore, when distilled and fine-tuned, Screener demonstrated strong performance on supervised segmentation tasks, establishing its value as novel pretraining method. Screener represents significant step towards comprehensive and scalable pathology de- tection, serving as a powerful unsupervised screening tool and a robust foundation for supervised applications.
Limitations future work. Despite its promising performance, Screener has several limitations that warrant future investigation. Its reliance the rarity assumption may lead to false negative errors for common or widespread pathologies, while statistical anomalies that lack clinical sig- nificance, e-g. artifacts, could result in false positives (though analyze robustness to artifacts, low-dose and contrast agent in Appendix (H). Comprehensive evaluation of UVAS methods remains challenging due to the lack of ground truth annotations for all potential pathologies. Currently val- idated on CT, the generalizability of our approach to other medical imaging modalities requires further exploration. Future work will also explore scaling laws to investigate how performance scales with model size and training data, potentially unlocking further improvements.
ACKNOWLEDGMENTS
This work was supported by The Ministry of Economic Development of the Russian Federation in accordance with the subsidy agreement (agreement identifier 000000C31322SP4HOM0 grant No 139-15-2025-012).
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A DICE SCORES UNDERESTIMATTIO IN UNSUPERVISED SETTING
In Figure we give examples of reasonable Screener's predictions for which Dice scores cannot be calculated properly due to incomplete ground truth masks.
Calcified nodule surrounded by lung opacity Ground truth mask contains only nodule annotation Screener's prediction include both nodule and opacity
Pleural effusion unnanotated in ground truth mask but detected by Screener
Tumor unnanotated in the ground truth mask but detected by Screener
Consolidation unnanotated in the ground truth mask but detected by Screener
Figure 4: Examples of Screener's true positive predictions (third row) counted as "false positives" due to incompleteness of the ground truth masks (second row), leading to Dice score underestima- tion.
B SELF-SUPERVISED LEARNING
InfoNCE. As in SimCLR Chen al. 2020), batch of positive pairs { (1) ) passed
through trainable MLP-projector Goons and L2-normalized: =! gawes (v: (k) )/ // (v) (k) I|l e Rdd
where 1,2 and = I.....N. .N. Then, the objective is to maximize similarity in positive pairs while minimizing similarity in negative pairs. To this end, InfoNCE loss is written
N
exp((z))) (1) 13 12
(2) (1))
min 1=1 (1,2)
log
(1)
exp((z))
(2) )/T) Ejjz Eie(1,2) exp(1z)") (2))
VICReg.
VICReg objective consists of three terms:
min Line + B v + Lov
(2)
The first term enforces embeddings to be invariant to augmentations:
inv
N Σ 1 22)
(3)
The second term ensures that individual embeddings' dimensions have a least unit variance:
Σ
max (0, -Vo! +
(4)
D 1=1
kE (1.1
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The third term encourages different embeddings' dimensions to be uncorrelated, increasing the total information content of the embeddings:
C
Σ
ΣD 1 D
Σ
(5)
kE(1,2)
In VICReg, embeddings {2(*0) are not L2- 12-normalized and obtained through a trainable MLP- expander which increases the dimensionality up 8192.
C BASELINE CONDITION MODELS
Sin-cos positional encodings. The existing density-basee -based UVAS methods (Gudovskiy et al. 2022 Zhou et al. 2024) for natural images use standard sin-cos positional encodings for conditioning. We also employ them we mean by sin-cos positional embeddings in CT images. Note that we never apply descriptor, condition or density models to the whole CT images due to memory constraints. Instead, at all the training stages and the inference stage of our framework we always apply them to image crops of size H x W S, described in (.1 33.3] When say that apply sin-cos positional emheddings condition model to an image crop, we mean that compute sin-cos encodings of absolute positions of its pixels w.r.t. to the whole CT image.
option for condition model in our framework. However, let clarify what
Anatomical positional embeddings. To implement the idea of learning the conditional distri- bution of image patterns each certain anatomical region, need condition model producing conditions c(pl that encode which anatomical region is present in the image at every position p. Supervised model for organs' semantic segmentation would be an ideal condition model for this purpose. However, to our best knowledge, there is no supervised models that are able to segment all organs in CT images. That is why, we decided try the self-supervised APE (Goncharov al.) 2024) model which produces continuous embeddings of anatomical position of CT image pixels.
D DESCRIPTOR AND CONDITION MODELS PROBING ANALYSIS
Our intuition - introduced in Section 3 and empirically validated Table 3 is that an effective conditioning variables should exclude information about presence / absence of pathologies, while capturing as much contextual information as possible (e.g., anatomical region, tissue type, patient- level characteristies etc.). This combination simplifies conditional density modeling and sharpens anomaly detection.
To make this intuition explicit, we conducted an additional probing experiment comparing the amount of pathology-related information retained by the descriptor model versus the condition model. We trained lightweight segmentation head on top of frozen descriptor vs. condition em- beddings and evaluated Dice scores on four pathology segmentation tasks. As seen in Table our condition model probing yields substantially lower Dice scores (p-value < 10-10 For lung cancer and liver tumors, scores are essentially zero, confirming that pathology-speciff in- formation is effectively removed. For pneumonia and kidney tumors, non-zer zerz Dice arises largely because these pathologies occupy substantial portions of the corresponding organs; the segmentation head tends to predict the whole organ, producing non-zero Dice scores.
10 for all datasets).
Table 5: Probing of frozen descriptor model (Dense VICReg) and condition model (masking- invariant Dense feature maps on supervised pathology segmentation tasks (results are Dice scores).
Model
LIDC
MIDRC
KITS
LiTS
Descriptor model probe
0.19 + 0.23
0.40 + 0.19
+ 0.17
0.25 + 0.23
Condition model probe
0.00 = 0.00
0.27 + 0.16
0.06 + 0.10
0.01 + 0.03
To demonstrate that our condition model indeed captures anatomical information better than base- lines, also probe it liver segmentation task on LITS. As in Table 6 condition model
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embeddings separate liver vs. non-liver voxels substantially better than APE and sinusoidal posi- tional encodings.
Table Probing our masking-invarian condition model vs. APE vs. sin-cos positional embed- dings on supervised liver segmentation task (results are Dice scores).
Model
LiTS (liver)
Condition model probe
0.82 0.14
APE probe
0.62 = 0.13
Sin-cos pos. embed. probe
0.36 0.14
E DENSITY MODELS
Below, we describe simple Gaussian density model and more expressive learnable Normalizing Flow model. Gaussian marginal density model is written as
log Gorm (1) (U 11) Σ Ey
n1) log det E const,
where the trainable parameters @dens are mean vector μ and diagonal covariance matrix >. Conditional Gaussian density model is written
log Qoden (y 2 (y) Migieeo (c)) (Egiee (c)) (y Mighon (c)) log det Egere (c) const,
where Higden and Eganm are MLP nets which take condition c I R input and predict condi- tional mean vector Magame (c) Ricter and vector of conditional variances which is used construct the diagonal covariance matrix Egen (c) E ge
As described in Section 3.3 both training and inference stages, we need obtain dense negative log-density maps. Dense prediction by MLP nets Maiee (c) and Mg (c) can be implemented using convolutional layers with kernel size 1 1 In practice, we increase this kernel size x 3, which can be equivalently formulated as conditioning on locally aggregated conditions.
Normalizing flow model of descriptors' marginal distribution is written as:
1 7 -1 f (v)
O S (y)
log Pakn (y)
1| log det
+ const,
where neural net fo must be invertible and has tractable Jacobian determinant. Conditional normalizing flow model of descriptors' conditional distribution given by:
log Poices (y c) 1 c)ll log det f (y. const,
1
(9)
Dy
where neural net fo: te x I dd
tm: must be invertible w.r.t. the first argument, and the
second term should be tractable.
We construct fe by stacking Glow layers (Kingma & Dhariwal 2018): act-norms, invertible linear transforms and affine coupling layers. Note that both training and inference stages we apply fa to descriptor maps E xxxxxxxx"** in a pixel-wise manner to obtain dense negative log-density maps. In conditional model, apply conditioning in affine coupling layers similar to Gudovskiy cet al. (2022) and also in each act-norm layer by predicting maps of rescaling parameters based on condition maps.
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F DATASETS
We utilized several publicly available datasets for training and evaluation summarized in Table 7 For training, we used the NLST (Team 2011), AMOS (Ji al. 2022), and AbdomenAtlas (Qu al, 2024) datasets. NLST data access is controlled by the National Cancer Institute Data Access Com- mittee and is available for research use. AMOS is released under a Creative Commons Attribution- NonCommercial Saarriii--ShhreAAee Shareellike 4.0 International License (CC BY-NC-SA 4.0). AbdomenAtlas is li- censed under CC BY-NC-SA 4.0 and intended for academic, research, and educational purposes. For evaluation, we used the LIDC-IDRI (LIDCC) (Armato III et al. (01T). MIDRC-RICORD-IL (MIDRC) (Tsai et al. 2020). KiTS (Heller et al. 2019). and LiTS (Bilic et al., 2023) datasets. LIDC-IDRI is available through The Cancer Imaging Archive (TCIA) and is typically used under terms permitting research and education. MIDRC-RICORD-I is also available through TCIA un- der similar terms, permitting non-commercial use for research and education. The KiTS dataset (version 2021) is available under a CC BY-NC-SA 4.0 license, primarily for non-commercial re- search and educational purposes. The LiTS dataset is available for research purposes, often under Conooe-oooooommiaaaa-aaaoooo
4.0 International License (CC BY- NC-ND 4.0) similar terms, specified by its organizers. We have used all datasets in accordance with their specified licenses and terms of use.
Creative Commons
Table 7: Summary information on the datasets that we use for training and testing of all models.
Annotated pathology
Dataset
# 3D images
NLST (Team 2011)
25,652
AMOS (Ji et al.
2022)
2,123
Abdomen Qu et al.
2024
4,607
LIDC (Armato III et al.
2011
1,017
lung cancer pneumonia
MIDRC ( ct al. 2020
115
KiTS (Heller et al. 2019
298
kidney tumors
LITS
Bilic et al.
2023)
117
liver tumors
G IMPLEMENTATION DETAILS
We preprocess CT volumes by cropping them to dense foreground voxels (thresholded by 500HUU, resizing to 1.5 x 1.5 x 2.25 mm3 voxel spacing, clipping intensities to [-1000, 3U, rescaling them to [0, range and applying CLAHE (Pizer et al. 1987) which ensures that color jitter aug- mentations preserve information about presence of pathologies during descriptor model training.
We train both the descriptor model and the condition model for 300k batches of m pairs of overlapping patches with N = 8192 positive pairs of voxels. The training takes about 3 days on a single NVIDIA RTX H100-80GB GPU. We use AdamW optimizer, warm-up learning rate from 0.0 to 0.0003 during first 10K batches, and then reduce it to zero till the end of the training. Weight decay is set to 10-6 and gradient clipping to 1.0 norm. Patch size is set to X x S 96 x 64.
During density model training, we apply average pooling operations with the 3 x 3 x 2 stride to feature maps produced by the descriptor model and the condition model, following (Gudovskiy et al. 2022, Zhou et al. (024). Thus h x w = 32 x 32 x 32. We inject Gaussian noise with 0.1 standard deviation both to the descriptors and conditions in order to stabilize the training. We train the density model for 500k batches each containing = 4 patches. This training stage again takes about 3 days on a single NVIDIA RTX H100-80GB GPU. We use the same optimizer and the learning rate scheduler as for the descriptor and condition models.
The modular Screener model has 133M parameters, patch-based inference for whole CT volume on NVIDIA RTX H100 GPU requires 4 Gb of GPU memory and takes about 5-10 seconds depending on the number of slices. The distilled Screener has 350M parameters, its patch-based inference requires Gb of GPU memory and takes 0.5-1.0 seconds. We did not observe any difference in quality metrics for the distilled model compared to the modular model.
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H ROBUSTNESS ANALYSIS
Voxel-level ROC curves low dose CTs vs. high dose
Voxel-level ROC curves for contrast CTS vs. non-contrast CTB
CT 0.964) contrast 0.956)
0.3
high IAUC 0.963)
60
0.8
32
Figure 5: Comparison of Screener's voxel-level AUROCs on high-dose vs, low-dose and on contrast vs. non-contrast images from LIDC dataset.
Figure 6: Examples of Screener performance on low-dose CT and artifacts. First row: Screener successfully segments lung cancer in low-dose CT. Second row: Screener assigns high anomaly scores to artifact.
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I ANALYSIS OF RECONSTRUCTION-BASS MODELS
Autoencoder
f-AnoGAN
Input image
Reconstruction
Errors
Reconstruction
Errors
Figure 7: Reconstructions and anomaly maps predicted by Autoencoder Baur et al. (2021) (second and third columns) and F-AnoGAN[Schlegl al. (2019) (last two columns), Autoencoder overfits to reconstruct pathologies and thus fails to detect them. Also Autoencoder produces blurry generations, leading to imaccurate reconstructions and high anomaly scores fine details (e.g., vessels in the lungs). f-AnoGAN avoids generating pathologies, but the reconstruction quality still is insufficient, resuling in false positive errors. GANs known be unstable and sensitive to hyperparameters, necessitating careful tuning and experimentation to achieve optimal results.
Patched Diffusion Model after training epoch
Patched Diffusion Model after 7 training epochs
Input image
Reconstruction
Errors
Reconstruction
Errors
Figure 8: Reconstructions and anomaly maps predicted by Patched Diffusion Model Behrendt et al. 2024) at different epochs. Note that at the beggining of the training (after epoch) it reconstructs healthy regions better than pathologies. However, after 7 epochs, it begins to reconstruct pathologies well.
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J FULL-DATA FINE-TUNING
In the fine-tuning experiments (Table 2 Section 4.2) in order to amplify the effect of pretraining trained all the models on random data subsets consisting of 25 labeled images. Here, in Table we also provide the results of fine-tuning on full training datasets. As anticipated, the benefits of pretraining become substantially smaller. Noticeably, only supervised pretraining, VoCo (Wu al,, 2024). and MAE (Wald et al. (2025) show statistically significant improvements on some datasets. In contrast, Screener-basee pretraining does not yield gains when abundant labeled data are available.
Table Full-data fine-tuning results for Screener and other self-supervised pretrained models. We highlight statistically significant improvements (green) or declines (red) relative to nnUNet trained from random initialization.
Model
LIDC
MIDRC
LTT
(random init.) (isonsee
mnNNet (supervised pretrain)) Huang 45 2023
0.58 1 49% (p 0.11)
0.65 2% (p 0.12)
0.80 25% (p 0:01)
0.82 4% (p 0.06)
Grounding DINO Liu 2023
0.43
25% (p 0.01]
0.62 3% (y 0.01)
0.63 18% (p 0.01)
0.49 18% (p 0.01)
LVM-Med MM Nouven et
0.41 | 25%
0.01)
0.61 13% (y 0.01)
0.62 21% (9 0.01)
0.50 15% () < 0.01)
Model Geneil
0.01)
0.62 2%
0.75 4% 0.013
0.53
TTagy
0.01)
[p 0.01;
DAE
Nglamarau alale 2025
0.51 [p 0.08)
0.83 2% (p 0.01)
0.75 (5% 0.01)
0.51 0%% (p 0.01)
VoC
Wa aia 2022
0.57 (e- 0.01)
0.64 0% (p 0.33)
0.79 1 2% (p 0.00)
0.60 17% (p 0.37)
MAE
Wild 21 2025
0.55 1% 0.21)
0.66 1 3% (p 0.01)
0.82 5% (p 0.01)
0.58 1 (p 0.68)
Donse
0% 0.85)
0.83 2% 0.01)
0.77 18: 0.06)
0.58 13% 0.19)
0.31)
1% 0.46)
0.76 25 0.01)
2%
19


Reviews:
[{"review_id": "dLLEAQ65EE", "reviewer": "Reviewer_Waas", "summary": "The paper presents Screener, a fully self-supervised framework for unsupervised pathology segmentation in 3D CT. It reframes pathology detection as unsupervised visual anomaly segmentation (UVAS), under the assumption that pathological regions are statistically rare. The method enhances density-based UVAS by (i) learning dense voxel-level self-supervised descriptors tailored to CT data, and (ii) introducing masking-invariant contextual embeddings as conditioning variables within a conditional density model. The models are trained on 30k unlabeled CT volumes. Screener achieves state-of-the-art results on four public CT datasets (LIDC, MIDRC, KiTS, LiTS). When distilled into a single UNet and fine-tuned with limited labeled data, it matches or surpasses medical SSL baselines.", "questions": "- In the self-supervised learning settings, besides the medical models, can authors examine the performance of the generalized model, for e.g., the Grounding Dino technique, which can retrieve objects given prompt input and apply some threshold techniques to filter noise [3]? Incorporating such experiments is interesting and highlights the benefit or potential weakness of the current strategy.\n\n-  It would be great to see the advanced performance by applying the U-Net with pre-trained medical models during the distillation process, aiming to bridge the gap between current performance and other SOTA.\n\n\n[3] Grounding Dino 1.5: Advance the\" edge\" of open-set object detection.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Reviewer acknowledges the following contributions:\n\n **Addresses an important medical challenge**: The paper tackles the critical and realistic problem of detecting all pathological findings in 3D CT without requiring pixel-level annotation, which is a limitation that hinders clinical deployment of current supervised methods.\n\n **Interesting and well-motivated technical idea**: \n\n- The proposed use of self-supervised learning for both the descriptor and conditioning models is conceptually appealing. It enables the system to model normal anatomy and contextual expectations directly from large unlabeled data, thereby detecting deviations that correspond to abnormalities.\n\n- Furthermore, the integration of dense SSL with masking-invariant conditioning is simple but effective. It allows a simple Gaussian density model to perform on par with more complex normalizing flows, indicating that meaningful contextual embeddings can simplify anomaly modeling.\n\n**Broad evaluation**: Experiments across four diverse datasets demonstrate that Screener generalizes well across organs and pathologies. The unsupervised anomaly segmentation results are significant, with clear ablation studies supporting each component.\n\n **Clear presentation**: The paper is clearly written, logically organized, and easy to follow, making complex ideas accessible to both machine learning and medical imaging audiences.", "weaknesses": "I found the following weaknesses in the current manuscript:\n\n- **Core conceptual clarity**: The motivation for why optimizing the **conditional density model** $q_{\\theta}(y|c)$. helps detect abnormal regions, is not fully articulated. Since this is a central concept, further clarification and discussion would strengthen the theoretical grounding. For instance, authors need to provide a visualization of normal pixels near the abnormal regions and show how the heatmap behaves to provide insights.\n\n- **Experimental organization**: The unsupervised experiments could be better structured to highlight the benefits of dense SSL. For instance, baselines should be grouped into (i) image-level SSL models (e.g., autoencoder) as well as adding SOTA models like LVM-Med [1] (developed for ResNet-50 and suitable with U-Net) and (ii) dense SSL models (the current author compared), to clearly demonstrate that dense SSL drives the improvements. \n\n- **Supervised performance gap** – While competitive, the fine-tuned Screener in Table 2 lags behind certain SOTA supervised or SSL-pretrained models. The authors could explore initializing the distilled UNet with a pretrained medical backbone (e.g., RadImageNet or LVM-Med to enhance feature representations.\n\n- **Presentation detail** – Some result table formats (e.g., Tables 1–3) could be reformatted and polished for a more professional appearance and readability.\n\n[1] Lvm-med: Learning large-scale self-supervised vision models for medical imaging via second-order graph matching, NeurIPS 2023.\n\n[2] RadImageNet: an open radiologic deep learning research dataset for effective transfer learning.\" Radiology: Artificial Intelligence", "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": "VNLyaE63cU", "reviewer": "Reviewer_NAFn", "summary": "This paper proposes Screener, a self-supervised anomaly segmentation framework for 3D CT. The method combines dense self-supervised descriptor learning, masking-invariant conditioning embeddings, and density modeling to detect abnormal patterns. The model trains on over 30,000 unlabeled CT volumes. Screener is tested on four public CT benchmarks under both unsupervised and fine-tuning settings.", "questions": "1. Are there reasons why condition embeddings are fundamentally more informative than APE or sin-cos encodings?\n\n2. How is the threshold determined to obtain the segmentation?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The problem is clearly motivated, as a supervised method cannot capture all distributions of pathologies.\n\nThe integration of dense SSL for descriptors and SSL-learned conditioning embeddings looks novel.\n\nThe proposed model is tested on four public CT benchmarks under both unsupervised and fine-tuning settings. Strong and consistent performance is observed.", "weaknesses": "While the method is well-executed, the contribution may appear incremental as the dense SSL for voxel embeddings and conditioning is a straightforward extension of VICReg and conditioning replacing sin-cos encodings is conceptually natural. The paper would benefit from clearer articulation of why condition embeddings are fundamentally more informative than APE or sin-cos encodings beyond empirical gains.\n\nThe proposed method looks closely related to an existing method [1]. It would be beneficial if the authors acknowledge and contrast with the prior work.\n\nThe method produces per-voxel anomaly scores. A threshold is necessary to perform the final segmentation. It is unclear how this threshold is determined.\n\nReferences:\n\n[1] Seince, Maxime, Loı̈c Le Folgoc, Luiz Facury De Souza, and Elsa Angelini. \"Dense Self-Supervised Learning for Medical Image Segmentation.\" In Medical Imaging with Deep Learning, pp. 1371-1386. PMLR, 2024.", "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": "QoglRK1j5E", "reviewer": "Reviewer_otbi", "summary": "The authors propose Screener, an unsupervised visual anomaly segmentation (UVAS) method, which leverages dense self-supervised pre-training and masking-invariant dense feature conditioning variables as replacement for positional encodings. The authors train Screener on 30k unlabeled CT-volumes and outperform existing UVAS methods and self-supervised pretraining methods when fine-tuning their method.", "questions": "Q1: I am not sure if I missed it but how do you get from the anomaly maps to hard predictions as needed for DSC measurement? Is there some thresholding and if so, how is the thresholding done in the unsupervised and supervised setting?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "The method the authors propose, is really nice, as it leverages dense self-supervised pretraining, which is a good choice given their goal of dense downstream applications and their addition of their masking-invariant dense feature conditioning variables as replacement for positional encoding is also well motivated and creative.\nAdditionally the clarity of the paper is also very high, with the authors explaining the different aspects very well.", "weaknesses": "The majority of my criticisms hinge around two key points of the authors paper.\n\n1) The authors claim their method exceeds current self-supervised learning methods. However, the authors don't compare against MAE pre-training, which was shown to be the strongest SSL pre-training for 3D medical image computing in the medical domain in a recent benchmark [1]. In particular the chosen VoCo and SwinUNETR pre-training baselines were shown to be bad for segmentation in general, making this claim not substantiated.\n\n2)  The evaluation protocol of using just 25 training cases is limited. If the authors claim their SSL method to be overall useful for segmentation I would like them to additionally evaluate their pre-training against other pre-training methods on a full-data regime. This is largely, because pre-training methods in general appear to yield performance benefits in small-data regimes, however when more data is available it may not do so anymore. Having this information is crucial for practitioners to know which SSL method to choose given their data availability.\n\n\nIf the authors 1) include MAE pre-training as an additional SSL baseline (when finetuning) and 2) include a finetuning experiment in a  full-data-regime and 3) include a full-length nnU-Net training as reference, I will raise my score further.\n\n\n[1] Wald, Tassilo, et al. \"An OpenMind for 3D medical vision self-supervised learning.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2025.", "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)