Histopathology-Genomics Multi-modal Structural Representation Learning for Data-Efficient Precision Oncology
Authors: Kun Wu
Zhiguo Jiang
Xinyu Zhu
Jun Shi
Yushan Zheng
Venue: ICLR 2026 Poster (2026)
Keywords: multi-modal learning
histopathology image representation learning
genomic data
graph structure learning
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Fusing histopathology images and genomics data with deep learning has significantly advanced precision oncology. However, genomics data is often missing due to its high acquisition cost and complexity in real-world clinical scenarios. Existing solutions aim to reconstruct genomics data from histopathology images. Nevertheless, these methods typically relied only on individual case and overlooked the potential relationships among cases. Additionally, they failed to take advantage of the authentic genomics data of diagnostically related cases that are accessible from training for inference. In this work, we propose a novel Multi-modal Structural Representation Learning (MSRL) framework for data-efficient precision oncology. We pre-train a histopathology-genomics multi-modal representation graph adopting Graph Structure Learning (GSL) to construct inter-case relevance based on the data inherently. During the fine-tuning stage, we dynamically capture structural relevance between the training cases and the acquired authentic cases for precise prediction. MSRL leverages prior inter-case associations and authentic genomics data from diagnosed cases based on the graph, which contributes to effective inference based on the single histopathology image modality. We evaluated MSRL on public TCGA datasets with 7,263 cases across various tasks, including survival prediction, cancer grading, and gene mutation prediction. The results demonstrate that MSRL significantly outperforms existing missing-genomics generation approaches with improvements of 1.44% to 3.12% in C-Index on survival prediction tasks and achieves comparable performance to multi-modal fusion methods. The code and data are available at https://github.com/WkEEn/MSRL.

Published as a conference paper at ICLR 2026
HiSTOPATHOLOGY-GGNNOIII MULTI-MODAL STRUCTURAL REPRESENTATION LEARNING FOR DATA-EFFICIENT PRECISION ONCOLOGY
Kun Wu Zhiguo Jiang", Xinyu Zhu", Jun Shi', Yushan Zheng'
School of Engineering Medicine, Beihang University, Beijing, China.
"Image Processing Center, School of Astronautics, Beihang University, Beijing, China.
"Tianmushan Laboratory, Hangzhou, China.
"School of Software, Hefei University of Technology, Hefei, China.
ABSTRACT
Fusing histopathology images and genomics data with deep learning has signifi- cantly advanced precision oncology. However, genomics data is often missing due to its high acquisition cost and complexity in real-world clinical scenarios. Existing solutions aim to reconstruct genomics data from histopathology images. Never- theless, these methods typically relied only on individual case and overlooked the potential relationships among cases. Additionally, they failed take advantage of the authentic genomics data of diagnostically related cases that are accessible from training for inference. this work, we propose a novel Multi-modal Structural Representation Learning (MSRL) framework for data-efficient precision oncology. We pre-train a wwwpppatholgggggnmmiiio multi-modal representation graph adopting Graph Structure Learning (GSL) to construct inter-case relevance based the data inherently. During the fine-tuning stage, we dynamically capture structural relevance between the training cases and the acquired authentic cases for precise prediction. MSRL leverages prior inter-case associations and authentic genomics data from diagnosed cases based on the graph, which contributes effective infer- ence based on the single histopathology image modality. We evaluated MSRL on public TCGA datasets with 7,263 cases across various tasks, including survival pre- diction, cancer grading, and gene mutation prediction. The results demonstrate that MSRL significantly outperforms existing with improvements of 1.44% to 3.12% in C-Index on survival prediction tasks and achieves comparable performance to multi-modal fusion methods. The code and data are available at
generation approaches
INTRODUCTION
Histopathology whole slide images (WSIs) describe detailed visual information of morphology
features, cellular organization, and phenotypic characteristics, which considered the gold standard for the assessment of cancer (Shmatko et al. 2022 Dimitriou et al. 2019 Hegde et al. 2019 Shamai al., 2022; Kather et al., 2019). Genomies data provide quantitative molecular characteristies of
the inirroenviromeen, which is significant for precision medicine and targeted therapies (Zhang
al.
2021 Eraslan et af. 2019 Zhou et al. 2019 Jaume et al.) 2024a Kopp et al.) 2020)
et al.
2022b Recently, an increasing number of studies on costopathology-geee -egommics multi-modal
learning have shown superior capabilities in precision oncology by providing a more personalized
representation that comprehensively reflects the case's status (Chen al. 2020a 2021 Zhou &
Chen (hen] Jaume et al., 2024b). Fusing morphological information from histology and molecular information from genomies data is the prevalent paradigm of
algorithms (Vale-Silval
& Rohr 2021; Mobadersany et al. 2018 Weng et al., 2019) as shown in Figure [[].. However,
the acquisition cost of genomics data is still very high, resulting in the lack of complete paired histopathology-genmmee data in many practical inferences.
"Corresponding author; Yushan Zheng (e-mail: yszheng waaaeng@hhaaaccu.c.).

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Published as a conference paper at ICLR 2026
Condition
Encoddery
Reconetrugten Diaillaion (c) Generative
Trining data Fusion)
(a) Multi-modal fusion
(b) Auxiliary tasks
(d) Progosed MSRL
Figure 1: Differences between proposed MSRL and previous methods, where (a) shows mulli-modal fusion methods, (b) and (c) depict WSI-based genomics reconstruction and distillation methods, where dashed lines indicate components used only during training and not involved in inference, and (d) presents our proposed MSRL, which incorporates authentic genomics data from diagnosed cases assist gene reconstruction and inference.
To address this challenge, some researchers focus on utilizing the histopathology data to reconstruct the missing genomics features and then apply multi-modal framework for prediction. These methods typically follow two paradigms: (1) constructing auxiliary tasks during training to reconstruct existing genomics data and encouraging the model to build the associations between WSI and genomics features through reconstruction loss (Wu et al. 2024; Wang et al. 2025) or distillation loss (Xu et al.) 2025) as shown in Figure or (2) adopting generative approaches that condition on histopathology data inputs to synthesize missing genomies features (Zhou al.) 2025) as shown in Figure [ These approaches partially addressed the problem of missing the modality during inference. However, these methods suffer from the following two limitations: (1) Ignoring potential relevance among cases. Reconstruction based approaches typically rely solcly on the histopathology image of the individual case to predict genomics features and focused on intra-case multi-modal alignment while overlooking crucial inter-case relevance for cancer diagnosis. (2) Underutilization of available authentic genomics data. During inference, the genomics modality is entirely synthesized by these methods, and they neglect the authentic genomics data of diagnostically related cases that are accessible from training data. This limits the authenticity and informativeness of the generated features.
In the paper, we propose a novel histopathology-gennee genommiss Multi-modal Structural Representation Learning (MSRL) framework for data-efficient precision oncology. We adopt graph structure learning to pre-train a multi-modal representation graph to construct inter-case relevance based on the data inherently and enable dynamic graph construction for downstream tasks to achieve inference with the single histopathology image modality. The data-driven graph pre- pre-training captures relevance among diagnostic cases. We introduce authentic data as auxiliary structural guidance to exploit the information of genomics data in missing modality scenarios during inference. Furthermore, we fine-tune the WSI encoder with the guidance of the multi-modal representation graph and enhance its ability to capture fine-grain grained morphology characteristics for data-cfficient prediction with the single image modality. The main contributions of this work are summarized as follows:
1.
We propose a novel Multi-modal Structural Representation Learning (MSRL) frame- work that effectively addresses cancer diagnosis tasks in genomics data missing scenarios. MSRL facilitates the reconstruction of missing genomics features by leveraging multi- relevance among cases and jointly enhance the representation capability of the WSI encoder, which significantly promotes data-efficient precision oncology.
2. We pre-train a multi-modal representation graph via self-supervised graph structure learning large-scale pan-cancer histopathology-genee -enommicss dataset. The pre-trained graph captures structural relevance among cases and is further utilized to guide dynamic graph construction during downstream inference, which contributes to effective prediction with the single WSIs modality. The framework leverages authentic genomics data as auxiliary supervision to reconstruct the missing modality data for inference, effectively maximizing the utility of available molecular information.
3.
We evaluated our method on publicly available The Cancer Genome Atlas (TCGA) datasets for survival prediction and four precision diagnosis tasks. Experimental results show that our approach significantly outperforms existing methods designed for missing modality scenarios, and achieves comparable performance with multi-modal fusion methods. More- over, our method demonstrates superior generalization capabilities on the external CPTAC datasets, which highlights its potential for real-world clinical applications.
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2 RELATED WORK
Fusion-basse Multi-modal methods Multi-modal approaches that integrate WSIs with genomics data offer a more comprehensive and objective representation for cancer diagnosis. Chen et al. (202T) proposed the MCAT framework to learn how histology patches attend to genes for survival prediction. Zhou & Chen (2023) utilized two parallel encoder-decoder structures to align WSI and genomics representations. Jaume al. (024b) tokenized genomies data and used memory efficient multi-modal Transformer to model interactions between pathway and histology patch tokens for prognosis. Xu & Chen (2023) and Zhang et al. (2024) leveraged optimal transport and prototypical information bottleneck theory, respectively, to fully enable the transfer and integration of information between the two modalities.
Multi-modal methods with missing modality Existing multi-modal fusion methods generally rely on the assumption that complete histopathology-geen genomies data pairs are available. However, acquiring genomics data is often costly and impractical, which results in missing modalities in real- world scenarios. To address this limitation, recent studies have explored methods for reconstructing paired genomics features from available histopathology images. One approach involves an auxiliary task during training to reconstruct the missing genomics data. |aang et al. (2024b) (025) were the first to propose using initialized prompts to replace missing genomics data to enable interaction with histopathology features through the cross-attention module. The prompts reconstruct the missing modality representation guided by available genomics data during training, and then these learned prompts are subsequently applied for inference. [u et al. (2024) introduced a proxy gene- reconstruction branch guide the WSI encoder to extract genomies-related features from image modality. Zhou et al. (2025) proposed conditional Latent Differentiation VAE (LD-VAE) to generate the missing genomics data from WSIs. Xu et al. (2025) employed a learnable prompt to substitute for the missing gene data and leveraged the powerful LLM (Large Language Model) to distill prognostic knowledge into the prompt learning process. Jin et al. (2025) ensured the decoder's ability to complete the multi-modal information by integrating missing data reconstruction with knowledge distillation. These methods rely on individual case data to reconstruct genomics features, while overlooking inter-case relevance that could more effectively guide the recovery of missing modality. Moreover, during inference, they failed to leverage the auxiliary information of available genomics data, despite evidence (Shu et al. 2024) showing that existing database knowledge can significantly enhance cancer diagnosis. As a result, current approaches struggle to faithfully reconstruct high-density genomics information for effective inference.
Graph Structure Learning (GSL) Graph Neural Networks (GNNs) have demonstrated strong performance in modeling structural dependencies and supporting downstream inference. typical GNN takes as input a set of nodes and edges, where the edge set encodes structural relationships between nodes. However, in many real-world scenarios, such prior structures are difficult define or are contaminated by noise, for example, the relationships between cancer patients are not explicitly known. Such unreliable structures limit the representational power of GNNs.
To address this challenge. data-driven Graph Structure Learning (GSL) (Franceschi et al. 2019 Liu et al. (022a) has emerged as an effective paradigm. The core idea of GSL is to incorporate the learning of graph structure into the task-driven learning process. Unlike traditional GNNs with fixed edge sets, GSL dynamically learns the edge structure during training based on node features and constructs an optimized graph topology. Li et al. (2023) summarized a general GSL pipeline: where is the adjacency matrix and X is the node feature matrix- and outputs refined graph g" (X, A"), which is then fed into GNN for representation learning and task inference. Notably, the original adjacency matrix A is not required: the graph learner can construct A" adaptively from the node features. Liu et al. (022b), Li et al. (2022a), and/Zhao et al. (2023) introduced contrastive learning to enable unsupervised GSL, eliminating the need for task labels and allowing the model mine latent data correlations. Shen ett al. (2024) further applied unsupervised GSL to multiplex graphs, offering a new perspective for structure learning in multi-modal learning.
Graph Learner takes an initial graph 9 = (X, A} } as
METHODS
Our proposed framework consists of two stages: Multi-modal Structural Representation Pre-training and Fine-tuning. In the first stage, as shown in Figure 2 (a), we leverage TCGA pan-cancer cases with
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(a). Multi Multi-modal Structural Representazion Pre craining
Entoder
Structure Augmentttion
Mii
Patching
Extradling
Graph grmpmine Sinn
Hitto Hito-Structtre
Patch Features
Hitoopetholoyy WSI
Gone Structure
Group
Ceonomc Encoder
Structure Augmentation
Genomic data
Genomic groups
Multi-modil Representation Fine-tuning Inference
Augmeniation
Encoding
Gragh Börward
Prediction
Indicctrr
Graph Structure
Encorder
Prompt
GNN Encoder
Predfictio
Histopathology WSI
WSI
Patching & Extracting
Fused-feature Align
Graph Represietattion
Online branch view
Structure
Buffer
A
Eddh
Feature Map
Graph Structure
WISI Encoder
Encoder
GVN
Target view
Encoder
Genomie
Group Normalizaiion
Trair Eefennnc
Genomic data
Srai Only
Figure 2: The framework of proposed MSRL, where (a) illustrates the multi-modal structural repre- sentation pre-training with TCGA pan-cancer dataset and (b) shows the fine-tume fine-tuning dynamically construct graphs for current cases and cases from the buffer, which leverages inter-case relevance and available authentic genomics data for inference.
complete multi-modal data for self-supervised pre-training. This process aligns the representation spaces of WSI and genomics and simultaneously establishes multi modill inter-case relevance. The abundance of pan-cancer data facilitates Graph Structure Learning (GSL) in constructing more generalized relevance patterns. Furthermore, the aligned genomics encoder can be more effectively applied downstream tasks. the Fine-tuning slage, as shown Figure (b), we apply the pre-trained GSL to dynamically construct graphs for the current cases in the mini-bater and previous cases from the training buffer. This leverages inter-case relevance and utilizes available authentic genomics data auxiliary information for effective fine-tuning. Based the above, propose the Multi-modal Structural Representation Learning (MSRL) that jointly learn multi-modal case representations and the graph relevance among cases.
3.1 FORMULATION
Histopathology Representation For the k-th case, the WSI is cropped into m non-overlapping patches after segmenting the foreground tissue region. We leverage powerful pathology foundation model for patch-level feature extraction and WSI encoder initialization. Specifically. the ViT-giant pretrained on GigaPath (Xu et al. 2024) is used to extract patch features, denoted as X E Rm xdp where dp is the dimension of the patch feature. The Transformer- soasfoomer-bbeeed LongNet (Ding al. 2023 pretrained on GigaPath used to initialize the WSI encoder (H(+). The WSI representation is the [CI.S] token of 6H ( formulated h Rda, where d, is the dimension of WSI feature.
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Algorithm 1: Graph Structure Learner (GSL)
Input: The node features X E R Rxxx.
Output: Refined adjacency matrix A
E < X, and the learner contains L layers;
for
=1t0 to I. do
for to do 2 E
( where Hadamard product and is the learnable weight vector;
E (1= 111 where is nonlinear activation function; E (B :// The post-processing steps are
A poss.processing(S))
described in Appendix D.2
return Refined adjacency matrix A
Genomics Representation We collect bulk gene expression profiles for each case, where each profile contains thousands of expression values. Following prior work (Chen et al. 2021 Zhou & Chen 2023 Jaume al.) 2024b) and the biological functional grouping of genes, we divide the genes into groups (Liberzon et al.) 2015). Each group is encoded using an independent SNN network Klambauer et al. 2017). denoted as X E Raxd,, where d is the dimension of each grouped gene feature. The genomics feature of the case is the mean pooling of these groups formulated as g" E R where dg d2 = d.
AH). 9G (G,AG) and OF = [F, AF) representing the histopathology graph, gene graph, and fused multi-modal graph, respectively. Specifically, H { and the adjacency matrix Aн E [0, 1 is initialized by applying K-Nearest Neighbors (KNN) clustering on H, where is the total number of cases. The graphs GG and OP constructed in the same manner. We fuse multi-modal features with simple strategy: pk concatenate h²) E R2d, in order to evaluate the contribution of case-level structural representation learning rather than complex fusion techniques.
Graph Representation We initialize three graphs: GH
3.2 MULTI-MODAL STRUCTURAL REPRESENTATION PRE-TRAINING
We leverage the pan-cancer data and introduce self-supervised GSL to achieve discriminative and generalized multi-modal structural representation learning, as illustrated in Figure (a). The core component of GSL is parameterized graph learner that adaptively infers the optimal refined structure from the input node features (Li et al. 2023 Liu al. 2022b;
al. 2022a: Zhao al. 2023
Shen et al. 2024), which takes the initial graph as input. The definition of GSL are described in Algorithm Subsequently, we detail the role of GSL during pre- pre-training.
Intra-modality GSL The diagnostic relevance among cases is inherently reflected within the identical modality. We adopt a contrastive learning paradigm (Chen al. 2020b Tian et al. 2020) to uncover
intra-modality relevance among cases. For the pathology graph 9H = H,, AH), ). we input the node features into the pathology-specifit graph structure learner and obtain the refined adjacency matrix, denoted as A₁ GSLH(H) Additionally, and edge addition to generate an augmented adjacency matrix, denoted as Ag ang Then, both views passed through the satholegy-specific Graph Convolutional Network (GCN) encoder (Kipf & Welling 2016), which is formulated as follows
apply random edge dropout
ZH GCNH AH H) E R Kxd, Z GCNH aug H) E ERKxd,
(1)
2023 is employed as the intra-modal constraint to maximize the The InfoNCE loss (Liang et al.
agreement between the node representations of the same case and minimize the similarity between those of different cases within each modality. The loss is defined as follows
exp(sim (2k, 26 )/T)
LInfoNCE(ZH Z009)
Σ log
(2)
E exp(sim(zz. 2, (²²)/TT)
where sim(..) denotes the cosine similarity between two representations, and is a temperature hyperparameter. We accordingly obtain the structure construction for the genomics modality Zg and Zaug and the modra-modality structure learning loss function formulated as follows
Lintra = (CinfoNCE(Zut Z + LIMfONCE (ZG; (Z::
(3)
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Inter-modality GSL Histopathology and genomics describe case's status from different per- spectives. We introduce inter mider-modllity GSL to align their respective structures within unified representation space. The InfoNCE loss is utilized to maximize the agreement between the different modal representations of the same case, which is formulated as follows
Linter = 1 ((CinfoNCE(Zn ZG) SinoonC((2: Zn)).
mnseedmmdaliiy GSL MultMMMlooooo modia fusion representations provide a more comprehensive and ob- jective description of case information. Therefore, further introduce a structural constraint to ensure that the fused representations preserve pathology-specific and genomics-specife character- istics. The multi-modal structure learner is employed to refine the adjacency matrix, denoted as Ap = GS (F). Consequently, we obtain the graph-encoded multi-modal representations denoted as ZF GC F). We then employ the InfoNCE loss maximize the agreement between the uni-modal representations and the fused multi-modal representation within the same case, which formulated follows
Liused = -5 (CInfoNcE(ZP: ZH) + Lin.oNCC ZG)).
(5)
Based on the above components, the overall pre-training loss is formulated as Lgst = Lintra Linter + Lissed- The multi perpliipesseettivee constraints facilitate training robust multi-modal graph structure learners and construct an effective multi-modal structural representation graph.
3.3 MULTI-MODAL STRUCTURAL REPRESENTATION FINE-TUNE AND INFERENCE
In this stage, construct online-targe target mual-branch architecture along with buffer mechanism, as depicted in Figure 2(b). The online branch performs efficient task prediction using only WSI data. The target branch, which is only activated during training, receives complete multi-modal data and guides the WSI uni-modal input online branch to learn inter-case relevance representations, thereby completing the missing genomies information. Furthermore, the buffer stores the complete multi- modal representations of the training samples, serving as key component for incorporating authentic data to construct inter-case relevance. Subsequently, we detail the specifics of each component.
The Buffer mechanism In the missing gene inference scenario, we define the training dataset as D Dtrain = (X Xú, y" J. and the testing dataset as Diest (X; f, 2² Jam "3)",, where S₁ and S₂ denote the number of training and test cases, respectively, and y the task label. The feature buffer
initialized using the available authentic data of training cases, denoted as Douffer = {f* { =
{comculenate( (dh (X&), ÓH (X;)) where ΦG is the pre-trained genomics encoder of first stage and ΦH the WSI encoder of foundation model. The learned features of the target branch from authentic multi-modal data are utilized to update the buffer with the First-In-First-On (FIFO) strategy, which ensures the buffer representations remain up-to-date.
Dual-branch training with complete modalities The target branch utilizes the relevance derived from complete multi-modal training data to guide the online branch in learning the missing genomics employ mixup-based augmentations to
information. Inspired by prior work (Chen & Lu 2023), generate distinct input views for the online and target branches, respectively. We design the Inductor module address the absence of genomics data in the online branch, illustrated in the encoding phase of Figure 2(b). Specifically, the Inductor module adopts the same SNN network architecture (Klambauer et al. 2017) as the genomics encoder, but takes the WSI representation input and outputs a genomics prompt that serves as placeholder for the missing data. This prompt, combined with the WSI representation, constitutes the online branch's 's multi-modal representation. The missing gene information is subsequently completed through the construction of multi-modal relevance during the Graph Forward (GF) process, which is presented in Algorithm I
Hierarchical Alignment We introduce hierarchical losses to jointly optimize multi-modal represen- tation learning and structure learning, as illustrated in Algorithm II. Firstly, we impose constraints on the features derived both preceding and succeeding the graph learning phases, which ensures the stable and comprehensive learning of authentic multi-modal data representations by the online branch and is formulated as follows
Latign LinfoNCE(fff)) Lyattggn = LInfoNCE(Z,
(6)

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Algorithm 2: Multi-modal Structural Representation Fine-tune and Inference
[.Function Graph Forward (GF);
Input: Slide features h, Gene features g, Buffer Duufter
Output: f.A. A
f f concatenatee h)
F + Readout Douters f) Combining features of the current cases with the buffer.
the others
+ GCN(A", Fy t + GSL(F) // Definition of GSL as shown in Algorithm return f, A", Z;
Iiinnettuning procedure with complete modalitics:
Φн < pre-trained WSI encoder, ΦG <- pre-trained Gene encoder;
// Encoding
- Inductor(h), h 1++6((X) (x7")
// Online branch input
10
(Xo), hi t- (x,(()))
// Target branch input
7/ Graph Forward
A" Z G g, Dbutter;
A, Z GF uazer ( Duater);
13
Duuter <- update((Dmin f);
14
Calculate losses: f (f, f), Cogatiin (Z, 2), (A", A"), Lank (Z. y):
15
Inferencce procedure with WSI modality:
17
g <- Irdducto((h), h <- du (X,); A,A", 12< & G.outine (h, g, Duatter); Prediction with Z:
18
19
Moreover, we adopt sparsity-balancee binary cross-entropy (BCE) loss (Duan et al. 2024 to align graph structures between A' and À'. For the target graph, we assume there are zero and ci non-zero elements in A", where co > To balance the loss between zeros and non-zeros, we apply scaling factors on each element loss as follows
Lantiggn BCE(AC Ar) = ag Σ
66
C.
CJ. j=1
ao
Co
+ C1 a1
(7)
200
261
where _ and L denote the loss calculated for zero and non-zero elements in Ar. The joint loss calculated in the fine-tune stage is formulated as fine tane = Li align + Lg align + L. align + Ltask- The loss function used exclusively to update the online branch, while the target branch is updated using an EMA (He et al. 2020) strategy to prevent representation collapse.
Inference and Prediction with WSI modality We apply the online branch for task inference as illustrated in Algorithm[]III where the trained Inductor is capable of estimating the missing genomics data. The integration of authentic genomics data from the buffer assists in completion of the missing genomics and facilitates data-efficient inference based the single WSI modality.
4 EXPERIMENTS
4.1 DATASETS AND SETTINGS
Datasets: We collect and curate n=7,263 cases with WSI-gene pairs from the TCGA pan-cancer dataset containing 32 cancer subtypes across 12 primary tumor sites. We construct pre-training dataset including 6,361 cases without any diagnostic information MSRL first-stage. For fine- tuning, evaluate the proposed MSRL framework six TCGA cohorts and two external CPTAC cohorts. Details of fine-tuning datasets and prep-rocessing data are provided in the Appendix C
Experimental Settings: All test data excluded from the first-stage pre-training. We implement two model variants: MSRL excludes genomics data during fine-tuning, with no genomics input to the target branch, no Inductor in the online branch, and the buffer contains WSI embeddings rather than fused feature. MSRLmuti incorporates authentic genomics data the online branch and replaces the Inductor with the pre-trained Φg. We introduced with several foundation models to
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serve pre- wre-trained baselines for comparison (Shao al. 2025 Xu et al. 2024 Wang et al. 2024a
Ding et al.] 2025). All foundation models utilized their respective patch encoders and initialized the slide encoders with public pre-trained weights. Then, we fine-tuned the slide encoder across all downstream tasks to ensure the objectivity and fairness of the reported results.
The C-Index (mean + std) on five survival prediction tasks, where "h." and "g." indicate rely on WSI and genomics, respectively. The cyan background represents methods trained with multi-modality data but inference with WSI. The best, second-bes overall, and the best in cyan background results are highlighted in bold red, underlined bold, and bold, (fspectively.(::p-aall c0.05:::p-valus <0.01)
Table 1:
Modality
(N-377)
(N-150)
STAD (N-388)
(N-292)
COADREAD (N=298)
Overall
SNN
0,5588 + 0.0814
0 0.03%6
0.5784 = 0.0419!
0.5455 0u858!
0,5806 + 0.352
0.5708
CLAM al 2001
h.
0.5304 0.0178
0.5286
8.0766
0.5482 0.0221
0.5160 0.0331
0.5740 0.0088'
0.5394
ScdMIL 2e1
0.5351 U.0742!
0.5692
0.5404 = 0.0511
0.5280 0.057s
0.5814 D.0717
0.5508
WIKG Lree Hi4
h.
0.5531 +: L.020S
0.5827
0.09857
0.5617 2 0.09837
0.5303 0.03541
0.5904 :: 0.0817
0.5635
TrunsMLL
Shan 2 2021
h.
0,5632 à: 0.0273
0.5372
0.02935
0.5762 a 0.0464
0.5570 0.0276
0.6164 2: 0.09777
0.5685
PANTIER Song
h.
0,5712 0.06417
0.6208
0.09971
0.6219 a 0.05981
0.5594 0.05501
0,6101 0.0500
0.5967
FEATHER Stap
0.0040
0.5698
0.0271
0 a
0.5277 2.0300
0.008
CHIEF Wanz al.
h.
0.5006 0.0890
0,5762
0.0768
0.5668 e 0.061
5338 0.0838)
0.0672
0.5639
GiguPPil X
h.
0.5656 + 0.0091
0.6282
0.0191
0/6176 + 0.036
0 0,5580
0.5330l
0.6082 + 0.0180
0 0.59/4
TITAN Ding dla 412
h.
015755 + 0.0864
016182
0.07111
0/3306 : 0.08337
0.5652 0.E381
0.6138
0.30411
0.6017
MSRI
0.00211
0.6308
0.666 0,0271
0.02891
0.01
MCXT
Chan alll
g.th.
0.6058 a: 0.013U
0.6644
0.0122
0,7064 = 0.0262
0.0164 0.05361
0:08847
MOTCa X3 C1:D-A1A5
g.th.
0.6097 a: 0.0540P
0.6689 0.0671
0,2086 1 0.0522
0.6175 0.06371
0.6439 1: 0.03466
0,6507
CMTA
2hg &
hmm 70M4
g++h.
0.6110 1: 0.0098
0.6708 0.00254
0.3110 : 0.0090
0 0,6214_ 0.0470l
0.6550 1: 0.0177
0.6547
PTRD
2linng
0.6116 e 0.0018'
0.6738
0.0466
0.7188 a 0.02671
0 02644 0.04341
0.6578 0.0654
0.653
CVAl
Zhonet
g:th.
0,6210 à 0,6288 1: 0.018
0.6712
0,7201 a 0.02951
0.6302 0.0300
0.022
Sur/Puth
[uume 213 20/40
g+th.
0,6366
0.0209
0.7194 1 0.0524
0 0.0256
0,5712 1 0.01.0
0,6683
DisPru df 1025
g+
0.6267 + 0.0423
0.6931
0.0372
0.7097 e 0.033
0.6390 = 0.05801
0.6770 e 0.0479"
0.6691
MSRI
0.6368 + 0.0127
0.7011 0.002!
0.723 + 0.0411
0.6456 0.0261
01.89% 0.0301'
0.6794
G-HANee Wang
2025
g.th.
0,5306 0.01491
0.6418
0.01381
0.6722 0.04891
0.5770
0.02781
0.621 0.01ga1
0.6246
LD-CVAF Zhou
p.
0,5054 0.0104!
0.430
0.0146
0,038 = 0.0455
0 0.5960 0.02661
B.6200
0.2211
06313
DisPto Xi 0 al. 2025
g.th..hh.
0.005N + 100269
0.0734
0.0852
0/805 = 0.0244
0.055 0.6101
B.41N
# 0.3422
0.6414
MSRI.
g.hh..h.
0.6192 + 0.0184
0.6808
0.0277
0.7880 + 0.0623
0.6182 0.0015
0.6554 + 0.0166
0.6558
4.2
COMPARISONS WITH STATE-OF-THE-ART
4.2.1 1 SURVIVAL PREDICTION
Survival prediction aims to estimate the time-to-event outcomes for patients (Zadeh & Schmid 2020 Jaume et al. 2024b). We use the concordance index (C-Index) as the evaluation metric. The formulation of survival prediction and the calculation of the C-Index are provided in the Appendix D.1 Table presents the experimental results of the proposed MSRL compared with several state-of-the-art methods.
MSRL with WSI-only inference substantially outperforms existing uni-modal WSI methods. In particular, it achieves 5.91 % improvement in C-index over PANTHER, the state-of-the-art uni- modal WSI method without pre-training. Foundation models exhibit effective WSI representation capabilities pre- pre-trained by large-scale data. For example, TITAN leverages region- level text-image multi-modal pre- vre-training and significantly surpasses the performance of the remaining uni-modal training approaches. Nevertheless, MSRL still outperformed TITAN by 5.51%. 1.. These results demonstrate that MSRL. can further enhance the diagnostic utility of WSI in real-world scenarios.
MSRL addresses the challenge of missing genomics data during inference more effectively than existing methods. Reconstruction-based methods achieve significantly better inference performance than all unimodal approaches, which effectively address the challenge of missing genomics data for inference. G-HANet performed differential analysis on the original genomic sequences and then reconstructed the remaining sequences during training. However, the heterogeneity among test cases results in significant biases in the reconstructed genomic data. The Variational Autoencoder of LDDCVAF strugglee to the low-rank data distribution where the sample size is much smaller than the feature dimension (d = 768), resulting in significant noise during sampling in inference. These shortcomings result in G-HANet and LD-CVAE falling behind MCAT by 2.09% and 1.42% in C-index. MSRL avoids the rough situation that LD-VAE is stuck in and employs structure- uruectre-guided reconstruction based on authentic original genomic data, which contribute to improvements in by 3.12% and 2.45% compared to G-HANet and LD-CVAE, respectively. DisPro distills prognostic knowledge into the genomics prompt during training. However, its inference procedure relies on self- scores computed within each individual case select and aggregate tokens. This design overlooks
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holistic case-level representations and inter-case relationships, which results in c-index that 1.44% lower than MSRL's. In addition, DisPro performs multi-stage mi--oodal training, which makes sensitive to the data distributions of both modalities and weakens its generalization ability at inference time. As shown in Table AT DisPro's c-index drops by 4.77% when evaluated on out-of-domain data. In contrast, MSRL only 1. 1.02% performance decrease under the same setting, which exhibits strong generalization capacity.
Compared with multi-modal fusion methods, MSRL outperforms MCAT and CMTA by 1.03% and 0. %. respectively. MCAT enhances WSI representations with genomic data unidirectionally. ignoring WSI's impact on genomic features. CMTA aligns WSI and genomic representations, but encodes genomic data using a WSI-specific module (Shao et al.) (021) that disrupts the sequence structure. MSRL's inter-modality constraint and genomic-specife encoding address these issues, which contribute to performance comparable to advanced multi-modie fusion methods, and enable data-efficient prediction using only the single image modality.
MSRLmali achieves the optimal performance and outperforms the second-best method by 1.03% in C-Index. This demonstrates that the multi-modal structural representation graph introduced by MSRL effectively enhances the integration of WSI and genomic features for survival prediction.
Table 2:
The performance on four precision diagnosis tasks, where a cyan background represents methods trained with multi-modalityy data but inference with WSI, and the others are WSI uni- modal methods. The best and second- best results are highlighted in bold red and underlined bold, sespectively(f:p-vaaa <0.05:$:p-valur <0.01)
Model
AUC
BRCA exging (m-744)
F1 spom
ALC:
NBCLCstaging (em803)
P1 score
EOFR mutation (am27)
ALC
P1 sc000
AUC
HER2 (om482)
F11 spore
CLAM
Lastal 2001
1.577200086
0.535
0.990
0.557 0.0157
0,765
2.0263
0.702 0.0187
0,623 0.0122
0.0273
0.550200277
0.0377
0.997 0.0294
0.565 0..0411
0.779
0.705 0.0022
0.667 = 2.0149
0.0181
Trmn
0,009 00005
0.547= 0.00400
0.69 0.01821
0.572 0.025g)
0,500 0.0266
0.712 0.025
0.0255
0.0322
DSMIL
000077
0
010774
0.627 0.0125!
0.375 0.00741
0.13 000222
0.81
0,514 0.0245)
WIKG
061920008
0.507=10122
0.587 0.005))
0514 000011
(1733 0:01977
0/402 (0150)
0/342)
PANTHER
0.0124
0,574
0,0540
0.620 0.0022)
0.0191
0.0366'
0.0312
CHIEF
Ware al prote
0.602 0.0169
0.566 0.000)
B.35 0.02204
0.569
0.04481
0.504 200304
n.713
0.57
: 0.00
0,517 a 0.96375
GigaPa
UA25= of
0.57)
0.655 0,01801
0.900
0.01
O517 4003133
01743 000005
0A91
FRATHEX
F1 mils
0627 00055
0.571
0.665 0.015p1
0.995
0.01
0,16 40000
0/548 OF/040F
0.69 + 9ipsyl
0.54 + 0
ITTAN
Dai air 2ir5
-668-00046
0.581- 0045))
0.60 0.0036)
0614
0.01561
0122
0.511 O.015F
0.911 4 0.967
0.566 4 0.1535
MSRI...
0.00551 RA52
010211 0.5%6
BJ655 0.01131
0.01361 0.655
0.62 1.0200
0.75K
0.704 : 0.056)
0.590 1 0.0755
G.HANei
Wais rtal 20PS
0/0253
0,572 0.0.00
B.634 0.0416
0661420.0000
0.6302000881
0.715 = DJ942
0.575 2 0.243
LD CVAE
0646 0:0309
0,582
2 0.0231
0.650 0.0264
0.619 = 0.02791
9536 2 0.0215
0701777
9,217 DJ1244
6 2 0.028!
MSRI.
0.664 0.0065
0,593 00277
8.661 1 0.0107
1L638 L 0.0108
0.842 1 100306
IL770 1 0.0165
0.750 00023
0.696 1 0.024
4.2.2 PRECISION DIAGNOSIS
We conduct four precision diagnosis tasks on two cancer staging datasets and two molecular prediction dataset. The experimental results shown in Table 2 demonstrate that the proposed MSRL and its variant MSRL achieve the highest performance across all tasks. Specifically, MSRL outperforms the second- best LD-CVAE in the AUCs/F1 scores for the four tasks by 1.8%/1.1% 1. 1.1%/1.9% 0.6%/0.5%, and 1.3%/1.9% respectively.
Degradation of WSI encoder capabilities during gene reconstruction. The diagnostic criteria of cancer staging relies on morphological characteristics, which makes it mainly dependent on WSIs. In the two cancer staging tasks, G-HANet is 1. % lower than the WSI method, PANTHER, in the AUC. Moreover, LD-CVAE introducing genomics data during training, fails to achieve significant performance improvement. This indicates that existing gene reconstruction methods compromise the WSI encoder's representation ability due to noisy high-dimensional genomics data being introduced during training the WSI encoder from scratch, resulting in bias in morphological feature learning.
MSRL effectively enhances WSI encoder capabilities. GigaPath is the baseline WSI encoder in our method. The WSI unimodal MSRL improves F1 scores by 1.6%, 3.5%, 1.5%, and 1.3% across four tasks compared to the baseline and outperforms existing missing modality methods in cancer staging with MSRL pre-training. Introducing genomics data further improves MSRL, performance, which shows that MSRL pre-training effectively leverages the structural information of cases to enhance the WSI encoder representation. Authentic genomics data guidance constructs effective case-level relevance and further strengthens the WSI encoder.
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5 MODEL ANALYSIS
5.1 ABLATION ANALYSIS
Table presents MSRL structure ablation results on survival prediction. In particular, KNN (co- sine) and KNN (Euclidean) correspond to adjacency matrices that are statically constructed using cosine similarity and Euclidean distance, respectively, instead of being learned by the graph learner. MSRLenline huffer updates the buffer with online branch features rather than target branch features, and MSRLaandom_GII. denotes variant where the GSL is randomly initialized without pre-training.
MSRL. outperforms MSRI randonn GSI. across all datasets, which demonstrates the effectiveness of the pre-training. Moreover, MSRLrandom.ess performs better than both KNN-based methods, which suggests that GSL captures implicit and comprehensive diagnostic relevance, not only similarity as represented by KNN, The performance drop in MSRL builer demonstrates the effectiveness of introducing authentic data from the target branch to construct relevance during inference.
Table 3: The results of the structure ablation on five survival prediction (ttasets.(t:ppvvllll <0.05:::p-
value <0.01)
Model
BLCA (N-357)
BRCA (N-680)
STAD (N-318)
HNSC (N-392)
COADREAD (N-298)
Overall
KNN (Fuclidean)
0.5692 0.02931
0.6283
0.0211
0.6326 0.03611
0.5626 0.03061
0.6117 0.0118
0,60099
0.0549)
KNN (cosinc)
0,5774 +: 0.02341
0.6371 + 0.0257!
0.6414 1 0.0411
0.5681 +: 0.0313
0.6188 : 0.01-11
0.6086
0.0472)
MSRI masiess CSL
0.5984
0.0216
0.6694 0.03312
0.6812 0.0320
0.5988 0.025al
0.6368 0.01561
0.63669
0.0189)
MSRL
0.6094 +: 0.02221
0.6716 i 0.0307?
0.6948 1 0.04921
0.6036 1: 0.02751
0.6462 : 0.01-491
0.6451C 0.0107)
MSRL
0.6192 0.0184
0,6808 0.0277
0.7050 0.0523
0.6182 a: 0.0015
0.6554 0.0166
0.6558
Table shows that removing the alignment of the graph structure Esallign results in the most substantial performance drop, which confirms that the effectiveness and authenticity of the structure are fundamental to our framework. Notably, when the dataset size is relatively small, graph learning without structural constraints tends to suffer from instability and fails to achieve convergence. We performed the comprehensive experimental analysis generalization of MSRL, the pre-training loss components, the buffer mechanism, and the parameter settings in Appendix E
Table 4: Ablation results of the loss function during the fine-tuning on validation datasets.
Coaliign
BL. 3777
0,588
0.599 0.008)
0,58KL 0.023)
0.570 0.037)
0,564CL 0.043)
0.034)
BRCAIN-SO)
0.72
0.64LL 00228)
0.061EL (0011)
0.62K_ 0.049)
0.58X] 0.089)
0.37XCL 0.093)
0,59XL
0.080)
STADiN=318)
0.729
0.89(( 00335
0.703L 60026)
0.887 (002)
0.657C 0.072)
0.683] 0.0666
(955)
0.009LL 00013)
0.6E7CL 0.095)
0.388C 0.024)
0.36X 0.039)
0.35% 0,064)
0.38KL 0.033)
COADREADIN-298)
0.861
0.647L 00014)
0.658L 0.096)
0.63C 0.036)
0.558 1 0.1033
0,57KLL 0.084)
5.2
VISUALIZATION ANALYSIS
We visualize the pre-trained multi-modal pan-cancer graph in Figure A1 The results demonstrate that the self-supervised GSL not only captures the relevance among similar WSIs but also uncovers potential RNA-rclated connections across heterogeneous WSIs, which contribute to constructing a more comprehensive graph representation and enable efficient inference based on unimodal data. Fur- thermore. Figure A2 confirms that leveraging authentic data facilitates a more effective construction of inter-case relevance. A more detailed analysis is provided in Appendix B
CONCLUSION
The proposed MSRL framework jointly optimizes representation learning and structure learning. Extensive experiments TCGA demonstrate that MSRL significantly outperforms existing methods and effectively addresses the challenges of missing modality with case-level relevance construction. However, there some limitations in the current work as described in Appendix F In future work, we will further extend these analyses and refine the MSRL framework to broaden its applicability to more diverse scenarios.
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7 REPRODUCIBILITY STATEMENT
We believe that the MSRL framework is not only effective for histopathology genommics tasks but also holds research value for other hierarchical multi-modal problems, such as broader domains involving molecular structures and protein expression. To this end, provide a detailed code demo describes in detail the setup of the publicly available TCGA dataset used in our study, and Appendix [ specifies the task definitions, model parameters. and the software/hardwas waree environment adopted in the experiments, We hope these materials will sufficiently ensure the reproducibility of our approach.
in the supplementary materials. Appendix C
ACKNOWLEDGEMENTT
This work was partly supported by the National Natural Science Foundation of China (Grant No. 62571015 and 62171007), partly supported by the Beijing Natural Science Foundation (Grant No. 7242270), partly supported by the Anhui Provincial Natural Science Foundation (Grant No. 2408085MF162).
REFERENCES
Richard J Chen, Ming Y Lu, Jingwen Wang, Drew FK Williamson, Scott Rodig, Neal Lindeman, and Faisal Mahmood. Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Transactions Medical Imaging, 41 (4):757-770, 2020a.
Richard J Chen, Ming Lu, Wei-Hung Weng, Tiffany Y Chen, Drew FK Williamson, Trevor Manz, Maha Shady, and Faisal Mahmood. Multimodal co-attention transformer for survival prediction in gigapixel whole slide images. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 4015-4025, 2021.
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pp. 1597-1607. PmLR, 2020b.
Yuan-Chih Chen and Chun-Shien Lu. Rankmix: Data augmentation for weakly supervised learning of classifying whole slide images with diverse sizes and imbalanced categories. In Proceedings of the IEEÉ/CVF Conference on Computer Vision and Pattern Recognition, pp. 23936-23945, 2023.
Neofytos Dimitriou, Ognjen Arandjelović, and Peter D Caie. Deep learning for whole slide image analysis: an overview. Frontiers in medicine, 6:264, 2019.
Jiayu Ding, Shuming Ma, Li Dong, Xingxing Zhang, Shaohan Huang, Wenhui Wang, Nanning Zheng, and Furu Wei. Longnet: Scaling transformers to 1,000,000,000 tokens, arXiv preprint arXiv:2307.02488 2023.
Tong Ding, Sophia Wagner, Andrew H Song, Richard Chen, Ming Y Lu, Andrew Zhang, Anurag Vaidya, Guillaume Jaume, Muhammad Shaban, Ahrong Kim, et al. A multimodal whole-slide foundation model for pathology. Nature Medicine, pp. 1-13, 2025.
Shijin Duan, Ruyi Ding, Jiaxing He, Aidong Ding, Yunsi Fei, and Xiaolin Xu. Grapheroc: Cross- correlation autoencoder for graph structural reconstruction. Advances in Neural Information Processing Systems, 37:45409-45437, 2024.
Gökcen Eraslan, Ziga Avsec, Julien Gagneur, and Fabian J Theis. Deep learning: new computational modelling techniques for genomics. Nature reviews genetics, 20(7):389-40) 2019.
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He. Learning discrete structures for graph neural networks. International conference on machine learning, pp. 1972-1982. PMLR, 2019.
11

---

Published as a conference paper at ICLR 2026
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 9729-9733 2020.
Narayan Hegde, Jason Hipp, Yun Liu, Michael Hammert-Bucks Emily Reif, Daniel Smilkov, Michael Terry, Carrie J Cai, Mahul B Amin, Craig H Mermel, et al. Similar image search for histopathology: Smily, NPJ digital medicine, 2(1):56, 2019.
Maximilian llse, Jakub Tomezak, and Max Welling, Attention- blenttio-bbased deep multiple instance learning. In International conference machine learning, pp. 2127-2136. PMLR, 2018.
Guillaume Jaume, Paul Doucet, Andrew Song, Ming Yang Lu, Cristina Almagro Pérez, Sophia Wagner, Anurag Vaidya, Richard Chen, Drew Williamson, Ahrong Kim, et al. Hest-lk: A dataset for spatial transcriptomies and histology image analysis. Advances in Neural Information Processing Systems, 37:53798-53883 2024a.
Guillaume Jaume, Anurag Vaidya, Richard J Chen, Drew FK Williamson, Paul Pu Liang, and Faisal Mahmood. Modeling dense multimodal interactions between biological pathways and histology for survival prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 11579-11590, 2024b.
Cheng Jin, Fengtao Zhou, Yunfang Yu, Jiabo Ma, Yihui Wang, Yingxue Xu, Huajun Zhou, Hao Jiang, Luyang Luo, Luhui Mao, et al. Genome-anchored foundation model embeddings improve molecular prediction from histology images. arXiv preprint urXiv:2506. 19681, 2025.
Jakob Nikolas Kather, Johannes Krisam, Porppimol Charoentong, Tom Luedde, Esther Herpel, Cleo- Aron Weis, Timo Gaiser, Alexander Marx, Nektarios A Valous, Dyke Ferber, et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS medicine, 16(1);e 1002730. 2019.
Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv: 020.0,02290,220 2016.
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter. Self-normalizing neural networks. Advances in neural information processing systems, 30, 2017.
Wolfgang Kopp, Remo Monti, Annalaura Tamburrini, Uwe Ohler, and Altuna Akalin. Deep learning for genomics using janggu. Nature communications, 1 11(())3488, 2020.
Bin Li, Yin Li, and Kevin W Eliceiri. Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 14318-14328. 2021.
Jiawen Li, Yuxuan Chen, Hongbo Chu, Qiehe Sun, Tian Guan, Anjia Han, and Yonghong He. Dynamic graph representation with knowledge-awarc attention for histopathology whole slide image analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11323- 11332, 2024.
Kuan Li. Yang Liu, Xiang Ao, Jianfeng Chi. Jinghua Feng, Hao Yang, and Qing He. Reliable representations make a stronger defender: Unsupervised structure refinement for robust gnn. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp- 925-935. 2022a.
Ruiqing Li, Xinggi Wu, Ao Li, and Minghui Wang. Hfbsurv: hierarchical multimodal fusion with factorized bilinear models for cancer survival prediction. Bioinformatics, 3899):55772294, 2022b.
Zhixun Li, Liang Wang, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, al. Gslb: The graph structure learning benchmark. Advances in Neural Information Processing Systems, 36:30306-303 88, 2023.
Paul Pu Liang, Zihao Deng, Martin Q Ma, James Y Zou. Louis Salakhutdinov. Factorized contrastive learning: Going beyond multi-view redundancy. Advances in Neural Information Processing Systems, 122999992999 2023.
Louis-Philiper Philippe Moreney, and Ruslan
12

---

Published as a conference paper at ICLR 2026
Arthur Liberzon, Chet Birger, Helga Thorvaldsdóttir, Mahmoud Ghandi, Jill P Mesirov, and Pablo Tamayo. The molecular signatures database hallmark gene set collection. Cell systems, 1(6): 417-425, 2015.
Nian Liu, Xiao Wang, Lingfei Wu, Yu Chen, Xiaojie Guo, and Chuan Shi. Compact graph structure learning via mutual information compression. In Proceedings of the ACM web conference 2022, pp. 1601-1610, 2022a.
Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng. and Shirui Pan. Towards unsupervised deep graph structure learning. Proceedings of the ACM Web Conference 2022, pp. 1392-1403, 2022b.
Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Richard J Chen, Matteo Barbieri, and Faisal Mahmood. Data-efficient and weakly supervised computational pathology on whole-slide images. Nature biomedical engineering, 5(6):555-570, 2021.
Pooya Mobadersany, Safoora Yousefi, Mohamed Amgad, David A Gutman, Jill S Barnholtz-Sloan, José E Velázquez Vega, Daniel J Brat, and Lee AD Cooper. Predicting cancer outcomes from histology and genomics using convolutional networks. Proceedings of the National Academy of Sciences, (21(1))))E977-2777777 2018.
Gil Shamai, Amir Livne, António Polónia, Edmond Sabo, Alexandra Cretu, Gil Bar-Sela, and Ron Kimmel. Deep learning-based image analysis predicts pd-11 status from h&e-stained histopathology images in breast cancer. Nature Communications, 13(1 13))5,6733, 2022.
Daniel Shao, Richard J Chen, Andrew H Song, Joel Runevic, Ming Y Lu, Tong Ding, and Faisal Mah- mood. Do multiple instance learning models transfer? In Forty-second International Conference on Machine Learning, 2025.
Zhuchen Shao, Hao Bian, Yang Chen, Yifeng Wang, Jian Zhang, Xiangyang Ji, et al. Transmil: Transformer based correlated multiple instance learning for whole slide image classification. Advances in neural information processing systems, 34:2136-2147, 2021.
Zhixiang Shen, Shuo Wang, and Zhao Kang. Beyond redundancy: Information- un- supervised multiplex graph structure learning. In A. Globerson, L. Mackey, D. Bel- grave, A. Fan, U. Paquet, J. Tomezak, and C. Zhang (eds.), Advances in Neural In- formation Processing Systems, volume 37, pp. 31629-31658. Curran Associates, Inc., 2024. URL https::/pproceediinnng neurips wwtpppereaaleeeeeeeeeeee filees//aper//224/ Paper Conference, pdf
Artem Shmatko, Narmin Ghaffari Laleh, Moritz Gerstung, and Jakob Nikolas Kather. Artificial intelligence histopathology: enhancing cancer research and clinical oncology. Nature cancer, (9):1026-1038, 2022.
Tong Shu, Jun Shi, Dongdong Sun, Zhiguo Jiang, and Yushan Zheng. Slideged: Slide-based graph collaborative training with knowledge distillation for whole slide image classification. In International Conference on Medical Image Computing and Computer- Assistee Intervention, pp- 470-480. Springer, 2024.
Andrew H Song, Richard J Chen, Tong Ding, Drew FK Williamson, Guillaume Jaume, and Faisal Mahmood. Morphological prototyping for unsupervised slide representation learning in computa- tional pathology. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15566-1577,, 2024.
Aravind Subramanian, Pablo Tamayo, Vamsi K Mootha, Sayan Mukherjee, Benjamin L Ebert, Michael A Gillette, Amanda Paulovich, Scott L Pomeroy, Todd R Golub, Eric S Lander, et al. Gene enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43): 2005.
Yonglong Tian, Dilip Krishnan, and Phillip Isola. Contrastive multiview coding. In Computer EisionEECCV 2020: 16th European Conference, Glasgow, UK August 23-28, 2020, Proceedings, Part XI 16, pp. 776-794. Springer, 2020.
13

---

Published as a conference paper at ICLR 2026
Luis A Vale-Silva and Karl Rohr. Long-term cancer survival prediction using multimodal deep learning. Scientific Reports, 11(1):13505, 2021.
Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 911), 2008.
Xiyue Wang, Junhan Zhao, Eliana Marostica, Wei Yuan, Jietian Jin, Jiayu Zhang, Ruijiang Li, Hongping Tang, Kanran Wang, Yu Li, et al. A pathology foundation model for cancer diagnosis and prognosis prediction. Nature, (84(5)35) 978, 2024a.
Zhikang Wang, Yumeng Zhang, Yingxue Xu, Seiya Imoto, Hao Chen, and Jiangning Song. Histo- genomic knowledge distillation for cancer prognosis from histopathology whole slide images. arXiv preprint 1:Xi::44031104/ 2024b.
Zhikang Wang, Yumeng Zhang, Yingxue Xu, Seiya Imoto, Hao Chen, and Jiangning Song. Histo- genomic knowledge association for cancer prognosis from histopathology whole slide images. IEEE Transactions Medical Imaging, 2025.
Wei-Hung Weng, Yuannan Cai, Angela Lin, Fraser Tan, and Po-Hsuan Cameron Chen. Multimodal multitask representation learning for pathology biobank metadata prediction. arXiv preprint arXiv:1909.07846. 2019.
Kun Wu, Zhiguo Jiang, Xinyu Zhu, Jun Shi, and Yushan Zheng. Genomics-embedded histopathology whole slide image encoding for data-cfficient survival prediction. In Medical Imaging with Deep Learning, 2024.
Hanwen Xu, Naoto Usuyama, Jasprect Bagga, Sheng Zhang, Rajesh Rao, Tristan Naumann, Cliff Wong, Zelalem Gero, Javier González, Yu Gu, et al. A whole-slide foundation model for digital pathology from real-world data. Nature, 630(8015):181 1888, 2024.
Yingxue Xu and Hao Chen. Multimodal optimal transport-based co-attention transformer with global structure consistency for survival prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 21241-21255 2023.
Yingxue Xu, Fengtao Zhou, Chenyu Zhao, Yihui Wang, Can Yang, and Hao Chen. Distilled prompt learning for incomplete multimodal survival prediction. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 5102-5111, 2025.
Shekoufeh Gorgi Zadeh and Matthias Schmid. Bias in cross moos-entropy-basee training of deep survival networks. IEEE transactions on pattern analysis and machine intelligence, 4309):3126-3137, 2020.
Yilan Zhang, Yingxue Xu, Jianqi Chen, Fengying Xie, and Hao Chen. Prototypical information bottle- necking and disentangling for multimodal cancer survival prediction. In The Twelfth International Conference on Learning Representations, 2024.
Zijun Zhang, Christopher Y Park, Chandra L Theesfeld, and Olga G Troyanskaya. An automated framework for efficiently designing deep convolutional neural networks in genomics. Nature Machine Intelligence, 3(5):392 400, 2021.
Jianan Zhao, Qianlong Wen, Mingxuan Ju, Chuxu Zhang, and Yanfang Ye. Self-supervised graph structure refinement for graph neural networks. In Proceedings of the sixteenth ACM international conference on web search and data mining, pp. 159-167, 2023.
Yu Zhao, Zhenyu Lin, Kai Sun, Yidan Zhang, Junzhou Huang, Liansheng Wang, and Jianhua Yao. Setmil: spatial encoding transformer-bait based multiple instance learning for pathological image analysis. In International Conference Medical Image Computing and Computer-Assisted Intervention, pp. 66-76. Springer, 2022.
Fengtao Zhou and Hao Chen. Cross-modal translation and alignment for survival analysis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 21485-21494 2023.
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Jian Zhou, Christopher Y Park, Chandra L Theesfeld, Aaron K Wong, Yuan Yuan, Claudia Scheckel, John J Fak, Julien Funk, Kevin Yao, Yoko Tajiaa, et al. Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nature genetics, 51(6):973-980, 2019.
Junjie Zhou, Jiao Tang, Yingli Zuo, Peng Wan, Daoqiang Zhang, and Wei Shao. Robust multimodal survival prediction with conditional latent differentiation variational autoencoder. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 10384-10390 2025.
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APPENDIX
A THE STATEMENT OF LLMS USAGE
Here we explicitly declare that large language models (LLMs, e.g., the ChatGPT series) did not participate in any of the preliminary research work, including but not limited literature review, idea formulation, method design, code implementation, experimental design, data processing, result organization and analysis, or figure generation. Their involvement was limited solely to the final stage of manuscript preparation. specifically for text polishing tasks such as grammar and spelling checks, refinement of certain expressions, and minor LaTeX table formatting adjustments. Importantly, they did not contribute to early-stage tasks such as drafting the article outline or designing the paragraph structure.
B VISUALIZATION
B. THE PRE-TRAINED GRAPH OF PAN-CANCER DATASET
Drain
Eadacrie
Proppate
Heematopolett
Admordry
Ouncodlary
0.5
0..
0.3
04
Figure Al: The graph of the pan-cancer dataset constructed by the pre-trained fused-modal GSL, where each node denotes the WSI representation of case and the coordinates are clustered by t-SNE Van der Maaten & Hinton (2008), and each edge weighted by the Pearson correlation coefficient between RNA expression of cases denoted by the two nodes.
We cluster Van der Maaten & Hinton (2008) the WSI representations of TCGA pan-cancer dataset, which are encoded by LongNet Ding et al. (2023) pre-trained by GigaPath Xu et al. (2024). Then, the pre-trained fused-modal Graph Structure Learner (GSL) utilized construct the adjacency matrix of the dataset. The completed graph of pre-trained pan-cancer dataset shown as Figure AT where each edge is weighted by the pearson correlation coefficient between RNA expression of cases denoted by the two nodes. The following observations are summarized:
The foundation model demonstrates effective generalization ability. Despite the absence of GigaPath, it exhibits the capacity discern the morphological characteristics TCGA WSIs. The figure illustrates that the WSI representations of the same site aggregated into clusters.
the TCGA dataset in the pre-training dataset of
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The pre-trained GSL of proposed MSRL can efficiently capture the morphological relationships of the cases. The fused-modal GSL constructs denser edges for nodes within the same cluster, sparser edges for clusters between different sites, and also constructs denser associations for different clusters the same site that are far from each other. It indicates that the fused-GSL. can effectively capture the structural associations between cases in histo-morphology.
The pre-trained GSL of proposed MSRL can efficiently capture the structural associations of cases at the molecular level. The correlation between gene expression levels within given cluster is high, while the correlation between different clusters is low. Furthermore, the different gene expression levels of distant clusters from the same site reflect the genetic heterogeneity between cancer subtypes.
The molecular associations enhance the connectivity of the foundation model embeddings. The distant cases in the feature space are also linked, which are corrected by the influence of gene correlation. These connections enhanced by genomics data play crucial role in facilitating MSRL to achieve the precision multi- modal fusion.
B.2 VISUALIZATION OF GRAPH STRUCTURE LEARNER
Relnnd Adjezency metri from recontrccted Come Refineed Adjeancy matro from realtte Gere
Cimup
Surviaa (months)
(montis)
TCGA-F5-6702
15.06
2
TCGA-AD-6890
24.86
18.10
3
TOGA-CK-4947
17.80
TCGA-AU-3779
14.70
Group
No.
Caoe
Survisal Time
(1sontho)
Averape
11
TOGA-64-6297
83.53
TCGA-CK-6747
84.10
64.65
6571
42.93
14
TCGA-F4-6855
48.06
Group
Group 2
Trann
Figure A2:
The adjacency matrix heatmaps obtained by GSL based reconstructed and authentic genomic data, where each row shows edge weights between test cases and training cases. The survival times of two training groups are presented.
To further evaluate the effectiveness and authenticity of the structure learned by MSRL, we visualize the adjacency matrices generated by GSL from both authentic and reconstructed genomics data in COADREAD dataset, as shown in Figure A2 We input the WSI and genomics data of the test cases (n=58) into the target branch, and the output adjacency matrix of GSL is arranged in descending order based the column sums, retaining the top 30 columns obtain the "Refined Adjacency Matrix from Authentic Gene". Meanwhile, we input the WSI of the test cases into the online branch, reconstruct the genomics data, and obtain the output of GSL. The consistent 30 columns with the target branch are selected to generate the "Refined Adjacency Matrix from Reconstructed Gene". In Figure A2] each row of the heatmap represents the edge weights between test cases and training cases. The blue bars in the left histogram represent the survival time (in months) of the corresponding test cases, and the gray bars indicate that the case is deceased. Two observations are as follows: (1) The online branch GSL can effectively learn the structural relationships from authentic data. The heatmaps of both matrices show high consistency, especially in the high- weight edges (yellow areas). This indicates that the online branch's 's WSI-based Inductor can effectively reconstruct the authentic genomics data. And then GSL can build meaningful relationships between previously unseen test cases and real diagnostic training cases. This ensures that MSRL can perform efficient multi-modal task inference in real-world scenarios with WSI unimodality.
(2) Fine-tuned GSL can capture task-specific knowledge. GSL significantly separates the test cases into high-risk group (rows 41 to 51) and low-risk group (rows to 10), which show high association with group (columns 1 to 4) and group 2 (columns 11 to 13) of the training cases. The average survival time of cases in Group 1 is
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only 18. 10 months and it is 64.65 months in Group 2. This demonstrates that the fine-tuned GSL can learn task-related diagnostic information among GSL and effectively promote precision oncology.
C DATASETS
Dataset: We evaluate the proposed MSRL framework on six TCGA cohorts during fine-tuning. Specifically, we collect Bladder Urothelial Carcinoma (BLCA, n=359), Breast Invasive Carcinoma (BRCA, n=680), Stomach Adenocareinoma (STAD, n=318), Head and Neck Squamous Cell Carci- noma (HNSC, n=392), and Colon and Rectum Adenocarcinoma (COADREAD, n=298) for survival prediction. For precision diagnosis, we consider Non-Small Cell Lung Cancer (NSCLC) stag- ing (n=893), Epidermal Growth Factor Receptor (EGFR) mutation status (n=627), BRCA staging (n=944), and BRCA human epidermal growth factor receptor-2 (HER2) status prediction (n=482, subset BRCA staging dataset). We used 8 datasets for fine-tuning, and every dataset was split into training-val dataset (containing 3,607 cases) and testing dataset (containing 902 cases) with 4:1 ratio, following a five-fold cross-validatio dataset, compiled additional 2,754 cases from the TCGA pan-cancer database, which is along with the maining-val dataset to form the pre-training dataset (containing 6,361 cases). In total, this paper used 7,263 cases (6,361 + 902), of which the testing dataset had no overlap with either the pre-training dataset or the training-val dataset. The model achieving the best performance on the validation set selected for reporting results on the test set.
strategy. To ensure a sufficiently large pre-training
Additionally, we collected two public datasets from the CPTAC project for external evaluation. Specifically, CPTAC-HNSC dataset comprises WSI data and clinical survival information for 106 patients and CPTAC-BRCA dataset contains WSI data and diagnostic stage information for 111 patients. We ensured these datasets share no overlap with the TCGA project.
Histopathology image collection: All WSIs data come from 20x magnification hematoxylin and cosin (HtE)-st slides. We crop each slide into 256 256x256-pixel patches and extract 1536- dimensional features using the ViT-giant model pre-trained on GigaPath (Xu et al. 2024). The WSI representation of MSRL is the 768-dimensional [CI.S] token of output of LongNet (Ding al. 2023) pretrained on GigaPath. Among the compared foundation models, both CHIEF and FEATHER use ABMIL (Ilse et al., 2018) as their slide encoder, and TITAN adopts VIT slide encoder. Notably, all the non foundation model methods utilize the same patch encoder as GigaPath.
We collect raw genomics data for each corresponding case from TCGA. Following the Hallmarks resource in the Human Molecular Signatures Database (MSigDB) (Liberzon et al. 2015 [Subramanian al. 2005), we select 4241 genes and divide them into 50 groups and then apply log- normoaaaaizziin. Each group is encoded into a (68-dimensional feature with an independent SNN network (Klambauer al. 2017).
Genomics data collection:
D EXPERIMENTS
D.1 SURVIVAL PREDICTION:
Survival prediction is to estimate cases' time-to-event outcomes. Following previous research (7adeh & Schmid 2020), this task is defined by two components: censorship status and event time. Censorship status denoted as c, where c = 0 indicates the case's death was observed, and indicates the cases' last known follow-up. Event time denoted as f, representing the time between diagnosis and observed death if c = 0, or between diagnosis and last follow-up, i.e. survival time, if c = 1. We estimate event time by dividing the time into non-overlap intervals (tz-1,ty) for j E [1...., n.. based on quartiles of survival times (c = 1). and denote these intervals as Vj, rather than predicting the exact event time /. directly. This converts the problem into classification task with censorship. Then, each case is represented by (Z, yj: c). where is the representation of the case. We design classifier where each output logit ùز predicted by the network corresponds to specific time interval. Based on this, we define the discrete hazard function as
f Ihazard (yy (2) (ūz).
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where denotes the sigmoid activation function. fhazard (Z) gives the probability that the patient dies within the time interval (tz-1.ty) tz). We then introduce the discrete survival function as
II (1 fynzard(yee (ykel)), k=
faur (z))
which represents the probability that the patient survives up to the time interval (4 1. (j) A Afterwards, we construct the negative log-likelihood (NLL) survival loss (Zadeh & Schmid 2020) to optimize this task, as formulated follows
LNLL (Z (000)))))))))
Σ -c(t) log (fruev (1)
2((0))
(8)
+ (1 (()) ) log [Jeurv
(1j - 7
(9)
+ ( log
fhazurd
(10)
where, ND represents the total number of cases in the dataset. The loss ensures that the model assigns high survival probabilities to patients alive at last follow-up, correctly models survival up to death time for deceased patients, and accurately predicts the time of death when observed. A detailed mathematical explanation is provided in (Zadeh & Schmid 2020). We finally take the negative sum of all logits to predict a patient-level risk score, which used to categorize patients into different risk groups and to stratify them accordingly.
C-Index: The Concordance Index (C-Index) is a metric used evaluate the consistency between predicted ordered sequences and true sequences. In survival prediction, C-Index measures how accurately the model ranks cases according their survival times. The C-Index ranges from 0.5 where 0.5 indicates random prediction and indicates perfect prediction. Specifically, the C-Index calculates the proportion of all comparable case pairs for which the predicted order matches the ground-truth order of survival times. For a pair of cases (case;, case,), if case;'s 's survival time is longer than case,'s 's and the model predicts a lower risk for case; than for patient casej, this pair is called "concordant pair." The formulation of the C-Index is as follows
c-index D(D 1)
ΣΣ I(t, < (1 cy),
where I(-) is the indicator function, which takes the value if the argument is true, and 0 otherwise.
D.2 EXPERIMENTAL IMPLEMENTATIONS:
In the pre-training, graph with 6,361 nodes was constructed, where each node contains 768- dimensional WSI feature and a f68-dimensional genomic feature. The Graph Structure Learner employed 2-layer architecture, and the Graph Convolutional Network (GCN) consisted of 3 layers to output final node embeddings of 256 dimensions. The model was trained using the Adam optimizer with initial learning rate of le-4 for 400 epochs. During fine-tuning, the AdamW optimizer was applied for 50 epochs with a 10-epoch warmup. The learning rate followed a cosine annealing schedule with maximum learning rate of le-5 and minimum of 1e-7. To validate the generalization of MSRL and baseline methods on out-of- of-distribution cases, we directly applied models trained on TCGA-HNSS and SCGA-BRCA, to the EPTAC-HNSC and CPTAC-BRCE datasets for testing, respectively. All implementations were carried out in Python 3.9, PyTorch 2.0 and CUDA 12.4 on computer cluster with six Nvidia GeForce 4090 GPUs.
Post-processing of GSL: The post-processing aims to refine the sketched adjacency matrix into sparse, neg-tega,ive symmetric, and normalized adjacency matrix A The three steps are as follows:
1. Sparsification.TT The obtained similarity matrix is typically dense and requires sparsification. For each node, we employ a KNN-based method and retain the top K connected edges and set the remaining connections to zero, specifically as follows: S(S0) = (Sp Siy if Siy top-k(S.) else 0:
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2. Symmetrization and Activation. To ensure bidirectional connections between nodes and guarantee non-negative edge values, we perform the following additional process- ing: S ((eym) Osym (lact (S(*P))
(s(()r))) +o, (s(m)))) where (()) is the activation
function;
3. Normalization. To ensure the edge weights fall within the range [0, 1]. the final processing
step is as follows: A Inorm (S(sym)) D(sam) is the degree matrix of 5((ym))
D (eumm) 3 ((sum) (D (ssm)) - where
E SUPPLEMENTARY RESULTS AND DISCUSSION
E.I GENERALIZATION ANALYSIS
Our framework presents no additional difficulty in generalization. Genomic data from out-of- distribution (OOD) cases often suffers from complex sources and various protocols. Direct inclusion of such data can cause sigmificant batch effects. In contrast, our method utilizes solely WSI unimodal inference. Its generalization only rely on the representation capability of the slide encoder. Therefore, the generalization capability of our method when facing dil-of--ditributiin data is consistent with other WSI encoder- -aase inference frameworks and does not introduce additional challenges.
Our cross-modal retrieval strategy even boosts the generalization ability. Existing uni-modal inference methods typically focus on reconstructing raw genomic data for individual case, where the reconstruction performance is also affected by OOD issues. In contrast, MSRL models genomic correlations between cases within an aligned representation space and then use the real source-domann genomic representation for multi-modal prediction. This strategy naturally avoids the multi-source heterogeneity inherent in raw genomic data during training. Therefore, it ensures better generalization than reconstruction-basee methods during the inference phase.
We constructed new external datasets to fully validate the generalization of MSRL as shown in Table MSRL consistently achieved the best performance both external datasets. Notably, MSRLH secured the top results even under conditions limited to WSI unimodal training. In scenarios addressing missing inference modalities, existing methods like G-HANet and LD-CAVE suffered performance drops exceeding 5% and 7% on CPTAC-HNSC and CPTAC-BRCA, respectively. In contrast, MSRL showed minimal declines of only 1.02% and 1.62%. outperforming most foundation models. These results demonstrate the superior generalization of MSRL. The performance of MSRL significantly decreases after removing the pre-training stage. Its generalization capability is also greatly diminished, where the C-index drops over 6%. This practically validates the necessity of our pre-training approach. As DisPro's prompts are specifically designed for survival prediction, we therefore excluded it from the cancer staging tasks.
Table A1: The generalization analysis results, where CPTAC-HNSC and EPTAC-BRCA are external test sets and A denotes performance declines.
Method
Modality
TCGA-HNSC C-index
CPTAC-HNSC C-index
TOGA-BRCA AUC
CPTAC-BRCA AUC
PEATHER
0.5277
0.02244
0.627
0,5782
188%
CHIEP
0.5338 0.0088
0.5266 0.0301
-0.92%
0.602 0.0169
0.5008 :: 0.0198
1.12%
GigaPath
0.5580 0.0330
0.5278 1 0.0545
-3.02%
0.625 J: 0.0074
0.5221 1 0.0457
- 25%
TIIAN
0.5652 0.0381
0.1341 = 0.0610
3.11%
0.648 010044
0,0004 + 0.0541
3.86%
M
0.5676 0.0280
0.0125
2.15%
0.652 0.0096
0,0000 0.0401
4.21%
G-HANer
g-th. sh.
0:6770
5.32%
0.632
0.0263
0,5612
LD-CVAE
g g4h
11506001
02226
0.3118 1 06669
-5.42%
0.606
0,0809
0.5728 1 0.0309
-7. 77.32%
DisPro
-h.
D. 616005322 060000
0.5576 : 00992
-4.77%
MSRL wio Pro
0.5088 0.0255
0L:364
0.0488
-6.24%
0.630 0.0335
0.5856 + 0.0381
-5.34%
MSRL
0.0015
0.6080 0.0118
-1.02%
0.664 0.0263
0.6478 0.0416
-1.62%
E.2 BUFFER ANALYSIS
Resource consumption: Graph-based inference does not lead to significant additional resource consumption. We evaluated computational cost on 136 test WSIs of the BRCA dataset. We measured the model FLOPs and average time required of each WSI for patch feature extraction, and WSI encoding and task inference as shown in Table R2, Compared to other methods, our approach does not introduce notable increases in inference time. To simulate large-scale datasets, additionally
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increased the buffer size in multiples. The WSI inference time increased by only 0.607 seconds less than 2% of the patch feature extraction time, even with the 50X buffer size. Therefore, our method is not a bottleneck in terms of time resource consumption for practical applications.
Table A2: The results of resource consumption.
Patch Feature Extracting
WSI-level Encoding and Inference
Total
Model
Time
FLOPs
Time
FLOPs
Time
FLOPs
CMTA
34.87s
4.68
10 G
0.997s
9.78 x 10"
35.867s
4.68 x 10 G
SurvPath
34.87s
4.68
10 G
0.439s
2.26 x G
35.309s
4.68 x G
G-HANet
34.87s
4.68
10 G
0.4155
6.03 x G
35.285s
R: 4.68 x 109
LD-CVAE
34.87s
4.68
10
0.487s
2.65 x 10a
35.357s
4.68 x 10
MSRL(bufficr:434)
34.87s
4.68
10
0,534s
1.78 x 10a
35.404s
4.68 x 10 G
MSRL((buffcr:43440
34.87s
4.68
10 G
0.542s
2.45 x 10
34.412s
Fo 4.68 x G
MSRL((bfffcr:3880)
34.87s
4.68
10 G
0.701s
3.97 x G
35.571s
4.68 x G
MSRT.(buffe:=21700)
34.87s
4.68
G
1.141s
1.33 x G
36.01
R: 4.69 x G
Buffer size analysis: We additionally construct experiments assessing the impact of changing buffer size task performance shown in Table A3 Results indicate that smaller buffer size leads to weaker model performance. The performance reduction is more significant when the number of training samples is limited. When the buffer size zero (i.e., removing the buffer), the model performance drops significantly. Furthermore, our approach constructs structural correlations between the current case and historical cases. Consequently, removing the buffer leads the loss of functionality for both GSL and GCN. This demonstrates that introducing authentic data is key to MSRL's effective inference. It also shows that a sufficient data scale better assists the model in learning missing genomic information.
Table A3: The ablation results of the buffer size on validation datasets.
Buffer size
100%
75%
50%
25%
0%
BLCA (N=357)
0.607
0.595 (-0.012)
0.589 (-0.018)
0.573 (-0.034)
0.533 (-0.074)
BRCA (N=680)
0.672
0.669 (-0.003)
0.664 (-0.008)
0.653 (-0.019)
0.591 (-0.081)
STAD (N=318)
0.729
0.713 (-0.016)
0.705 (-0.024)
0.691 (-0.038)
0.611 (-0.118)
HNSC (N=392)
0.622
0.615 (-0.007)
0.611 (-0.011)
0.602 (-0.020)
0.545 (-0.077)
COADREAD (N=298)
0.661
0.655 (-0.006)
0.640 (-0.021)
0.621 (-0.039)
0.557 (-0.104)
E.3 PARAMETER ANALYSIS:
Pre-training data settings: We have added experiments to evaluate the effectiveness of H-G pairing and different scales of pre-training data. We constructed GSL. models using 20%, 50%, and 80% of the pre-training data, and we also constructed a GSL using a fully pre-trained dataset with random H-G pairings. Table A4 presents the validation metrics of the pre-training models for five survival prediction tasks under different settings. Based on these experiments, we draw the following conclusions:
1. Using more pretraining data effectively improves model performance. The model perfor- mance is optimal when using the full dataset, and the performance improvement from 50% to an 80% data increase is significantly higher than the improvement from 20% to 50%. This indicates that more data benefits the pretraining GSL, which is consistent with the scaling law.
2.
The pairing of H-G data is essential for multimodal research. The model performance using the fully pretraining dataset with random pairing is lower than that of the model pretraining with 20% paired data, and in some cases, the results are close to random predictions (for example, the c-index for HNSC and COADREAD was only 0.507 and 0.502, respectively). Compared with the method of training with WSI data only, H-G random pairing will introduce noise and reduce model performance.
We have added the ablation study for the three loss components used during pre-training as shown in Table AS] The results indicate that removing the individual loss L leads the most significant performance reduction. This is because Lfsse constrains the
Pre-training loss ablation studies:
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Table A4: The results on the validation set of the various data settings.
Data setting
BLCA
BRCA
STAD
HNSC
COADREAD
random pairing pre-training data
0.522
0.534
0.511
0.507
0.502
WSI only
0.574
0.593
0.582
0.536
0.524
20% pre-training data/1272 cases
0.615
0.688
0.609
0.551
0,533
50% pre-training data/3181 cases
0.627
0.694
0.613
0.556
0.541
80% pre-training data/5089 cases
0.647
0.720
0.644
0.577
0.548
100% pre-training data/6331 cases
0.651
0.744
0.658
0.593
0.558
multi-modal representation of the cases, playing vital role in integrating multi-modal information for the downstream tasks. Linter is the key constraint for aligning the cross-modal representations of the same Lintra enhances the discriminative power of the individual modal data representations. Removing any of these losses negatively impacts the downstream task performance.
Table A5: Ablation results of the pre-training losses on validation datasets.
Cinter
Cintra
fuse
HEAA
(10025)
(10.036)
0.567 (10.040)
0.522 (J03055)
(10093)
0,35 (10.88)
BRCA
0.672
0.640 0.640 (10.032)
0.646 (10.026)
0.636 (10.0366
0.538 (30.134)
0.524 0.524 (10.148)
0.563 (10.109)
STAD
(10.07))
(10.089)
(10.083)
(10.169)
0.557 (10.172)
0,585 (10.144)
HNSC
0.622
0.603 (10.019)
0.594 (10.028)
0.569 (10.053)
0.530 (10.092)
0,536 (10.086)
0.559 (10.063)
COADREAD
(10.040)
(10.032)
(10.048)
(J0.116)
0.543 (10.118)
0.573 (L0.085)
shows the validation metries of the prognostic tasks for different values of K. The results indicate that the model is relatively robust to variations in K (with metric fluctuations within 0.02). Overall, as the value of K increases, the model can gather more relevant case support, which leads to higher performance. However, the computational complexity also increases accordingly. Considering the balance between performance and resource consumption, we ultimately selected K=12 for all experiments.
K value ablation studies: Table A6
Table A6: The results on the validation set of the various values in GSL.
K
BLCA
BRCA
STAD
HNSC
COADREAD
4
0.633
0.728
0.639
0.579
0.546
8
0.648
0.734
0.644
0.582
0.551
12
0,651
0.744
0.658
0.593
0.558
16
0.654
0.747
0.659
0.602
0.562
E.4 MISSING MODALITY TRAINING
We additionally constructed experiments detailing different genomic data missing rates. We did design the method handle modal omission during the initial stage. Therefore, conduct this experiment, we adjusted the training strategy for the Target branch. If a case lacks genomic data, the Target branch does not participate in training; only the WSI-input Online branch trained. The specific experimental results are shown in Table A7 Even when using only partially complete data, MSRL still surpasses all uni-modal training methods. Furthermore, MSRL remains superior to existing methods for uni-modal inference, can fully utilize existing multi modal data to learn comprehensive and robust representations. This greatly enhances WSI's diagnostic value and data efficiency during the inference phase.
a 30% missing rate. This demonstrates that MSRL
E.5 THE KM ANALYSIS OF SURVIVAL PREDICTION
To further assess the effectiveness of MSRL in survival prediction, we divide all patients into low-risk and high-risk groups based on the median of the predicted risk scores from MSRL. Then, we apply Kaplan-Mett MMeer analysis Additionally, we conduct a Log-rank test to evaluate the statistical significance between the low-rist group (blue) and the high-risk group (red). A p-value of 0.05 less is considered statistically
visualize the survival outcomes of both groups, shown in Figure A3
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Table A7: The results of different genomic data missing rates.
Method
Modalit
BLCA
BRCA
STAD
HNSCC
COADREAD
Overall
G-HANeL
g-th.
0.580620.0149
0.6114000138
0.678220,0489
0.5770
0.621620.0184
0.6246
LD-CVAE
g-th. th.
0.5954=0.0104
0.6430-1.0146
0.6938=0.0495
0.5960=0.0286
0.628040.0211
0.6313
8.th. th.
0.608840.0269
0673440.00322
0600340.0424
0.603300.0610
0.6080000322
MSRL missing 60%
g.4b.
0.65
0.691820.099 0.650800.0577
0.589630.0570 70
0.618620:4401
0.6303
MSRL missing 30%
g-
0.613240.066 0668
0.675240.0809
O. 0.704400.0830
0.602610.0505
0.644810.0587
0.6478
MSRL missing 0%
g. -h.
0.6192=0.0184
0.6808=0.0227
0. 7050=000523
0.618240.0015
0.655440.0166
0.6558
significant. The results show that p-values of all datasets are much smaller than 0.05, indicating significant differences between the groups.
Kaskan Nexs Surveea Curves with Lay Tox (1
Eapten Mider Surandl Cureeo wilh Hars hot 9938
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Kaplan Surviel Tet 193%
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Figure A3: The curves of Kaplan-Meier analysis for all survival prediction datasets and the p- p-vulue of the Log-rank test.
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Published as a conference paper at ICLR 2026
F LIMITATIONS OF MSRL
There are three limitations of the current work: (1) Jointly optimizing node representations and graph structures easily leads to local optima, which makes difficult to achieve task-optimal solutions. We proposed that pre-training and fine-tuning partially mitigate this challenge, but still need to find a more stable optimization strategy during fine-tuning. (2) We have not yet introduced more structured textual information during MSRL's pre-training Incorporating clinical reports could help MSRL learn more robust structural representations in multi-modal task inference. (3) We have not yet conducted thorough analysis of the distribution of medical centers, ethnic composition, and regional characteristics in the TCGA dataset, nor have we examined their potential impact on the applicability of our method.
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Reviews:
[{"review_id": "HRRI3PlzvI", "reviewer": "Reviewer_xzsK", "summary": "This paper proposes a graph structure learning-based approach to fuse genomics with histology for survival prediction. The approach aims to capture the relationship between cases by constructing a graph per modality and between modalities. It consists of a pre-training phase and fine-tuning phase. The pre-training phase learns graph structures for each modality, with contrastive loss preserving intra-modal representations that are invariant to slight pertubations to the graph structure, as well as inter-modal representations that seek to align nodal representations across modalities. Meanwhile, the fine-tuning phase trains two branches: an online branch which processes only the histologic image and generates the genomic representation with an SNN, and a target branch which processes both the WSI and true genomic representation. A hierarchical alignment module encourages the histology-only online branch to have the same output as the histology+genomic target branch. During inference, only the online branch (histology only) is used. \n\nThe method, called MSRL, is pre-trained on histology+genomic data from TCGA, and evaluated on a subset of these cases for survival prediction. Comparison against 13 total benchmark approaches (genomics-only, histology-only, histology + genomic,, and inferred genomic) demonstrate that MSRL consistently outperforms existing approaches across five survival tasks and four diagnostic tasks. \n\nAblation studies investigate relevant questions, including how MSRL performance changes when exposed to histology only versus both modalities at inference. The model is also benchmarked against a sufficient number of tasks and methods. However, pre-training and evaluation are performed entirely on cases from TCGA. Their method may be particularly susceptible to distribution shift due to its reliance on generated representations and encoding of all patient samples in their generated graph, and should therefore be evaluated on external cohorts such as CPTAC as well. \n\nAdditionally, MSRL utilizes extensive pre-training for the slide encoder, but performs benchmarks entirely against randomly initialized models. Additional comparisons with open-weight pre-trained slide encoders should be performed.", "questions": "While no cases from evaluation were used during pre-training, were the cases also stratified by patient? If a patient has multiple cases divided across pretraining and validation cohorts, then their genomic information may still be encoded in the pretraining stage. The splits used for pretraining and evaluation should be shared.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "- The method is benchmarked against an appropriate number of tasks and organs\n- The method is benhmarked against an appropriate number of approaches across a range of histology-only, histology + genomics, and inferred genomic approaches, demonstrating meaningful improvements compared to these benchmarks.\n-  The proposed method is novel and addresses a new method to incorporate genomic information through a graph-based approach. \n- The ablations are well-selected and provide helpful insight into the effect of each component on performance.", "weaknesses": "- Pre-training and validation is performed entirely on cases from TCGA. Performance on out of distribution cases, from cohorts external to TCGA, should be investigated. It is possible that MSRL, which both retains all sample-level embeddings from pretraining in the form of nodes, and generates simulated genomic information, will struggle to generalize to new clinical distributions. \n- While MSRL utilizes pre-training, it is benchmarked against randomly initialized approaches. MSRL should also be benchmarked against existing lightweight slide encoders such as THREADS, CHIEF, and FEATHER. \n- The methods consist of many steps and moving parts. The section is difficult to follow, with many elements introduced without sufficient motivation or context, described below:\n- Line 224-226: Please describe the post-processing steps in the appendix\n- Line 231-232: Clarify the meaning of this sentence. The motivation behind using contrastive learning with intra-modal InfoNCE is not clear. In particular, please clarify that the goal is invariance between each respective node, and why this invariance is desirable. \n- Line 280: Please define the purpose of the “feature buffer” and what it represents.\n- Line 276-277: Please clearly describe the purpose of the online branch and target branch. \n- Line 289-290: Many new terms are introduced here. Please define more clearly either the Inductor module or the meaning of a “genomics prompt”. \n- Line 295: Please define the meaning of the $Readout$ operation\n- Line 305: make more clear what the hierarchical nature of this loss is. \n- Line 312: clarify how alignment constraint helps learn “robust representations”. What about this loss makes the online branch more “robust”?\n- In addition to describing the design motivations more clearly in the methods, the motivation for using GSL is not clearly stated in the introduction. \n- There is no mention of GSL or graphs in related works", "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": "s34j0clO5f", "reviewer": "Reviewer_iCa8", "summary": "This paper proposes a multi-modal structural representation learning (MSRL) framework to address the issue of missing modality (it is genomics in this paper). It consists of a pre-training stage and a fine-tuning stage. In the first stage, i.e., pre-training, paired histology-gene data is collected to train GNN encoders for different modalities (including gene, histology, gene-histology fused) via a self-supervised graph contrastive learning strategy, where one node represents the corresponding features of one patient. In the second stage, a dual-branch fine-tuning strategy is adopted. It uses an online branch that takes histology WSIs as input to generate gene representation and fuses multi-modal representations (original histology + generated gene features) for prediction. To ensure the quality of gene representation generation, fused-feature, structure, and graph-level alignment are imposed, with an EMA-based model updating technique. Experiments on five datasets are conducted and their results show that the proposed framework obtains the best overall performance in compared baselines.", "questions": "- What do the authors mean by 'data-efficient' in the title? If it refers to missing data, designing new experiments (different modality-missing rates) would be better?\n- Line 87: Could the authors explain in which scenarios genomic data is needed for cancer diagnosis? In most cases, histopathology WSIs (as the gold standard) are sufficient, right?\n- Could Figure 2(b) be further improved to show how the missing modality is addressed more clearly? The current version is not clear enough.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The motivation of MSRL is clearly presented. Moreover, the analysis of the limitations of existing methods is rational.\n- The proposed framework considers the potential relevance between different cases, which remains understudied in existing works.\n- The core idea of the proposed framework seems interesting as it employs the WSI as the condition to generate gene features with several alignment constraints to ensure the generation quality.", "weaknesses": "My major concerns as as follows:\n- The presentation of the Methods part: This part introduces many notations, individual steps, loss functions, new concepts, and new terminologies, making it hard to follow. The authors are strongly encouraged to improve it by carefully organizing each step in the algorithm and making sure their representation is self-contained and can convey information more efficiently.\n- Quite-complicated solution: The proposed framework, overall, is complicated, with many techniques (self-supervised graph contrastive learning, mixup-based augmentation, dual-branch updating, etc) stacked, making it look like an over-engineering scheme.\n- Unclear technical contributions in the first stage: It is unclear which algorithms are proposed by this work.\n- Insufficient experimental evidence: the authors mentioned that their framework is proposed for a data-efficiency purpose. However, the experiments are missing. One could say this framework is not data-efficient as it requires pre-training on large-scale paired data. Furthermore, the authors should design new experiments (different modality-missing rates) to verify the data-efficiency advantage of the proposed framework. Besides, a state-of-the-art baseline (Xu et al., Distilled Prompt Learning for Incomplete Multimodal Survival Prediction, CVPR 2025) is not compared. The authors could also add some general, representative methods (those also address modality missing issues) for comparisons to increase the soundness.\n\nIn summary, this paper still needs substantial improvements in terms of presentation and key experimental designs to support its claims.", "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": "G7wVDAxrLU", "reviewer": "Reviewer_EbNx", "summary": "This paper proposes MSRL, a framework that integrates histopathology whole-slide images (WSIs) and genomics data to improve precision oncology, especially when genomics data are missing. Unlike prior methods that reconstruct genomics features from single cases, MSRL leverages inter-case relationships through graph structure learning to model structural relevance among patients. Based on large-scale evaluation from TCGA across survival prediction and precision diagnosis tasks, MSRL outperformed existing missing-modality reconstruction approaches in C-index and achieved performance comparable to full multi-modal fusion models.", "questions": "Please see the Weaknesses section.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1.\tIntroduces a Multi-modal Structural Representation Learning (MSRL) framework that uniquely combines graph structure learning with histopathology–genomics fusion, enabling inference from WSIs alone by leveraging inter-case relationships.\n\n2.\tConducts extensive experiments on over 7,000 TCGA cases across multiple cancer types and tasks (survival prediction, staging, and mutation status), with comparisons to both unimodal and multi-modal baselines.", "weaknesses": "1.\tWhile the paper provides a detailed overview of existing reconstruction-based solutions for handling missing genomics modalities, the discussion omits another important line of research — knowledge distillation or modality-distillation frameworks that transfer multi-modal information into a single-modality model for inference [1-2]. These approaches (e.g., modality distillation or teacher–student setups trained with full modalities but deployed with WSIs only) address the same practical problem more elegantly, without explicitly synthesizing genomics data. Including and contrasting these methods would strengthen the contextual positioning of MSRL, clarify its unique advantages over distillation-based solutions, and provide a more balanced view of the current landscape of missing-modality learning in computational pathology.\n\n$\\quad$ [1] Jin, Cheng, et al. \"Genome-Anchored Foundation Model Embeddings Improve Molecular Prediction from Histology Images.\" arXiv preprint arXiv:2506.19681 (2025). \n\n$\\quad$ [2] Xu, Yingxue, et al. \"Distilled Prompt Learning for Incomplete Multimodal Survival Prediction.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n2.\tThe paper states that MSRL leverages authentic genomics data from diagnostically related cases during inference through graph structure learning. However, it remains unclear how this mechanism avoids potential information leakage between training and inference phases. In a realistic clinical setting, test patients may come from a distribution distinct from the training cohort, and their “related” cases’ genomics data would not be available. The manuscript would benefit from clarifying whether the model restricts graph construction to training data only, how it ensures that no test information is indirectly used, and whether any domain-shift analysis (e.g., across cancer subtypes or institutions) was performed to assess robustness when patient distributions differ. Without this clarification, it is difficult to judge the practical feasibility and generalizability of using authentic genomics profiles as auxiliary signals during inference.\n\n3.\tThe paper pre-trains the MSRL model on a large-scale TCGA pan-cancer dataset and then evaluates it on TCGA subsets (e.g., BRCA, STAD, HNSC, COADREAD). While the authors mention that “all test data are excluded from the first-stage pre-training,” the training and evaluation still originate from the same overarching dataset and share highly similar data distributions. This setup risks information leakage or at least unfairly optimistic performance, as patient-level overlaps, stain distributions, or cancer-specific morphological priors could be implicitly retained from pre-training. To ensure a fair assessment of generalization, it would be valuable to include evaluations on external cohorts or at least cross-center TCGA splits to verify that the model’s performance is not inflated by intra-dataset correlations. \n\n4.\tThe AUC values reported for BRCA and NSCLC staging tasks (around 0.66) are surprisingly low compared with prior WSI-based studies, where standard MIL models such as ABMIL often achieve near-perfect discrimination (>0.95 AUC) [1] for such coarse-grained classification tasks. This discrepancy raises concerns about dataset construction, label definition, or preprocessing consistency. The paper should clarify whether these staging labels were derived from TCGA clinical metadata (which may be incomplete or imbalanced), or whether additional filtering or downsampling was applied. Without this clarification, it is difficult to interpret the reported improvements or compare them meaningfully with prior literature.\n\n$\\quad$ [1] Chen, Richard J., et al. \"Towards a general-purpose foundation model for computational pathology.\" Nature medicine 30.3 (2024): 850-862.\n\n5.\tThe literature review in this work is not thorough enough. First of all, for the distillation-based missing modality modeling:\n\n$\\quad$ [1] Jin, Cheng, et al. \"Genome-Anchored Foundation Model Embeddings Improve Molecular Prediction from Histology Images.\" arXiv preprint arXiv:2506.19681 (2025). \n\n$\\quad$ [2] Xu, Yingxue, et al. \"Distilled Prompt Learning for Incomplete Multimodal Survival Prediction.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\nMeanwhile, for fusion-based multi-modal methods:\n\n$\\quad$ [3] Xu, Yingxue, and Hao Chen. \"Multimodal optimal transport-based co-attention transformer with global structure consistency for survival prediction.\" Proceedings of the IEEE/CVF international conference on computer vision. 2023.\n\n$\\quad$ [4] Zhang, Yilan, et al. \"Prototypical information bottlenecking and disentangling for multimodal cancer survival prediction.\" arXiv preprint arXiv:2401.01646 (2024).\n\nMoreover, the MIL methods used for comparison did not show in the Related Work section.", "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": "E4SqJq0lKO", "reviewer": "Reviewer_vXzx", "summary": "The authors propose MSRL, a novel architecture for multi-modal survival analysis and modality completion. To address the limitations of prior methods, the underutilization of gene data and the neglect of inter-patient relationships, this paper introduces a new approach that trains a gene encoder using self-supervised graph learning. The framework is validated on both modality completion for survival analysis and WSI classification.", "questions": "Explain why it is reasonable to directly connect WSI and genes using kNN while the large differences between the two modalities may lead to unbalanced mapping.\nThe experimental validation would be significantly strengthened by additional ablation studies. Specifically, an ablation on the individual components of the pre-training loss is required to dissect their respective contributions. Additionally, the role of the buffer component should be explicitly clarified, and its impact empirically validated", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The proposed method aligns well with its stated motivation, offering targeted solutions for the challenges in modality completion. The staged training approach is novel, consists of first pre-training a gene encoder with self-supervised graph learning, followed by aligning encoders trained on single modalities. The experiments are comprehensive, covering two downstream tasks and including KM-plot analysis and GSL visualizations in the supplementary materials.", "weaknesses": "The paper directly uses cross-modal kNN to construct cross-modal maps. However, due to the inherent differences between WSI and genes, it is difficult to guarantee the balance of this mapping method.\nThe ablation studies are missing experiments on the losses used during the representation pre-training. Additionally, I am curious about the function and impact of the \"Buffer\" component.", "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": "0IdwOp57RB", "reviewer": "Reviewer_vuS4", "summary": "This paper introduces Multi-modal Structural Representation Learning (MSRL), a novel framework to address the issue of missing genomics data in precision oncology. The core contribution is a two-stage approach: first, it pre-trains a graph structure learner on paired WSI-genomics data to capture inter-case relationships. Second, during fine-tuning and inference, it leverages this learned structure and a feature buffer of authentic training cases to guide predictions from WSI data alone. The method demonstrates improved performance over existing reconstruction-based approaches on survival prediction and other diagnostic tasks.", "questions": "1. **On Buffer Management:** Could you elaborate on the long-term strategy for the feature buffer? Given that a static buffer may become outdated in clinical practice due to data distribution shifts, have you considered dynamic updating mechanisms like a FIFO queue or more advanced sampling strategies (e.g., based on uncertainty or diversity) to maintain its relevance?\n\n2. **On Fairness of Comparison:** The comparison to \"isolated\" inference baselines may be unfair due to MSRL's \"non-isolated\" use of a feature buffer. To better isolate the contribution of your proposed structural learning, would you be willing to add a stronger, paradigm-aligned baseline? For example, one that uses a standard image retrieval model to find K-Nearest Neighbors and averages their ground-truth genomics data for reconstruction. How do you expect MSRL would perform against such a baseline?\n\n3. **On Robustness to Heterogeneity:** The experiments are confined to the standardized TCGA dataset. How would MSRL's Graph Structure Learner (GSL) perform in the presence of significant batch effects from heterogeneous real-world data? Have you considered or can you discuss how the model might distinguish true biological signals from technical artifacts during graph construction?\n\n4. **On Interpretability of the Learned Structure:** Could you provide a concrete case study from your results to demonstrate the model's ability to uncover non-obvious, clinically relevant relationships? For example, can you highlight a pair of histologically dissimilar patients that were strongly connected by the GSL and explain the potential underlying molecular basis for this connection?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "The primary strength of this paper lies in its novel reframing of the missing modality problem in computational pathology. Instead of treating each case in isolation, the authors introduce a structural, context-aware paradigm. The originality stems from the creative integration of self-supervised Graph Structure Learning (GSL) with a dual-branch fine-tuning strategy, effectively creating a system that learns how to leverage a knowledge base of related cases for inference. This is a significant departure from standard reconstruction methods. The quality of the work is supported by extensive experiments on multiple TCGA cohorts, demonstrating not only superior performance but also the value of each component through thorough ablation studies.", "weaknesses": "**1. Methodological Concerns on Scalability and Buffer Management:**\n\nThe core mechanism of MSRL relies on a feature buffer of historical cases during inference, which raises concerns about its long-term viability. The paper lacks a discussion on the strategy for maintaining and updating this crucial component over time. The current approach seems to imply a static buffer constructed from the training set, which would quickly become outdated in a real clinical setting where new data arrives continuously and data distributions may shift (e.g., due to new equipment or patient demographics). To address this, the authors should elaborate on a long-term maintenance strategy. For instance, would a First-In-First-Out (FIFO) queue be appropriate, or could a more intelligent, uncertainty-based or diversity-based sampling strategy be used to keep the buffer representative and up-to-date?\n\n**2. Experimental Concerns on Fairness of Comparison and Robustness:**\n\nA critical issue lies in the fairness of the experimental comparison. MSRL's inference process is \"non-isolated\" as it actively queries a knowledge base (the feature buffer) of labeled training samples. In contrast, the baseline methods (e.g., G-HANet, LD-CVAE), while trained on the same data, perform inference in an \"isolated\" manner for each new case. This paradigm difference may grant MSRL an inherent and potentially unfair advantage. To disentangle the benefits of the proposed GSL-based model from the benefits of this \"open-book exam\" paradigm, the authors should implement a stronger, paradigm-aligned baseline. A simple yet powerful baseline would be to use a standard image retrieval model to find the *K* most similar cases for a new WSI from the training set, and then simply average their corresponding ground-truth genomic data to serve as the reconstructed features. This would help clarify whether the performance gain truly comes from the sophisticated structural learning or primarily from leveraging nearest neighbors' information.\n\nAdditionally, the study's reliance on the relatively standardized TCGA dataset limits its generalizability. Real-world clinical data is notoriously heterogeneous, suffering from significant batch effects. It is unclear how robust MSRL is to such noise, as the GSL module could be misled by technical artifacts rather than true biological signals. The authors should strengthen their evaluation by conducting cross-cohort or cross-institutional validation experiments to provide a more convincing assessment of the method's real-world applicability.\n\n**3. Lack of In-depth Interpretability Analysis:**\n\nWhile the paper's title emphasizes \"Structural Representation Learning,\" the analysis of the learned structure remains superficial. The core strength of this method should be its ability to uncover non-obvious, clinically meaningful relationships between patients. The authors should conduct several in-depth case studies to enhance interpretability. For instance, can they showcase a pair of patients with histologically distinct tumors (perhaps even from different cancer types) that MSRL connects with a strong edge? If so, can this connection be explained by shared molecular pathways, genetic mutations, or similar treatment responses documented in biomedical literature? Such analysis would greatly elevate the paper's scientific impact.\n\n**4. Room for Improvement in Presentation and Clarity:**\n\nThe overall presentation of the paper could be significantly improved. The connection between the stated motivation and the proposed method is not immediately clear, requiring the reader to reread sections to grasp the core idea. The description of the methodology, along with the quality of the framework diagrams, could be refined for better clarity and intuition. A more direct and streamlined narrative would enhance the readability and accessibility of this otherwise promising work.", "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": "qSIa2zHcje", "reviewer": "Reviewer_GyVr", "summary": "This work proposes a Multi-modal Structural Representation Learning (MSRL) framework that pre-trains a histopathology-genomics multi-modal representation graph via graph structure learning to capture inherent inter-case relevance using paired TCGA pan-cancer data. Then, during fine-tuning, with the guidance of multimodal representation, an inductor is learned to reconstruct genomic features, which then serves as a proxy when genomic data is missing during inference.", "questions": "See weakness.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 1, "contribution": 3, "strengths": "1. The idea of structural alignment based on graph learning is new for addressing the problem of missing genomic data.\n2. The performance of the proposed method is better when using genomic data only during inference, although there is a gap compared to the model with complete multimodal data for inference.", "weaknesses": "1. It is hard to follow the manuscript with so many notations. Moreover, a lack of intuitive insights behind the model design makes it difficult to understand the problem the authors wanted to address.\n2. The related works are not comprehensive for both Fusion-based Multi-modal methods and Multi-modal methods with missing modality. For example, DisPro [1], a multimodal model for survival prediction that is robust to missing modalities, is considerably closer to the proposed method. It should be discussed and compared in the experimental section.\n3. So many important details are missing in the methodology section. For example, it is confusing how the buffer assists in the modeling. And what did Readout stand for? Did the model use complete data during training? Or both missing data and complete data.\n4. Data contamination issue. Given that the pretrained data is from TCGA and the finetuning datasets also come from TCGA, although they cannot see the label, there is still data contamination. External validation is necessary to validate the generalizable ability of the proposed method. For example, CPTAC is recommended to validate the model’s generalization.\n5. Given that the proposed method can handle both missing and complete modalities during inference, the performance of complete modalities should be presented for fair comparison to multimodal fusion approaches. This can deepen the understanding of the performance boundary of the proposed model.\n6. The ablation studies are incomplete.\n\n    1) The effectiveness of various loss components in the pretraining should be validated.\n\n    2) It is confusing about what MSRLonline_buffer represents.\n    3) Each component of alignment loss (fused feature, structure, and graph alignment) should be investigated.\n    4) The effectiveness of the buffer should be explored by removing it.\n    5) The target branch can be validated by removing it and the performance combination between the online branch and the genomic data.\n    6) To showcase the superiority of the proposed pretraining method, various pathology foundation models should be compared by replacing the pretrained weights, e.g. UNI [2], Virchow[3], and especially mSTAR [4], which also leveraged genomic data to enhance pathology.\n7. Given that the model can handle missing modality, various missing ratios should be investigated.\n\n[1] Xu Y, Zhou F, Zhao C, et al. Distilled Prompt Learning for Incomplete Multimodal Survival Prediction[C]//Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 5102-5111.\n\n[2] Chen R J, Ding T, Lu M Y, et al. Towards a general-purpose foundation model for computational pathology[J]. Nature medicine, 2024, 30(3): 850-862.\n\n[3] Vorontsov E, Bozkurt A, Casson A, et al. A foundation model for clinical-grade computational pathology and rare cancers detection[J]. Nature medicine, 2024, 30(10): 2924-2935.\n\n[4] Xu Y, Wang Y, Zhou F, et al. A multimodal knowledge-enhanced whole-slide pathology foundation model[J]. arXiv preprint arXiv:2407.15362, 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": "d1JD0reoO9", "reviewer": "Reviewer_sJW6", "summary": "This paper proposes a two-stage framework, MSRL for Robust inference in precision oncology when genomics is missing at test time. Specifically, in stage one, self‑supervised multi‑modal Graph Structure Learning (GSL) is used to learn inter‑case structure over WSIs, genomics, and fused features. In stage two, a dual-branch with an online branch (WSI only, learns a genomics “prompt” via an SNN Inductor) and a target branch (uses authentic genomics during training, updated via EMA) was used for finetuning. In experiments, MSRL outperforms WSI‑only methods and prior “missing‑genomics reconstruction” approaches by 2.45–3.12% C‑Index on survival.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1.\tClarity of high-level idea and task framing: The authors make clear the distinction between conventional reconstruction and their structure guided reconstruction augmented with authentic training genomics.\n2.\tMethodological coherence: The pipeline that (i) pretrains a multi‑modal structural graph; (ii) transfers it to task-specific fine‑tuning; and (iii) retrieves structural support from training cases at inference is well motivated. The dual-branch (online/target with EMA) is a sensible way to stabilize learning and avoid collapse, and the hierarchical alignment losses are thoughtfully designed.\n3.\tAblations and analyses: The KNN vs GSL ablations, loss ablations (especially showing $L_{salign}$ is critical), buffer source ablation, and buffer size/inference time analysis are useful and strengthen the paper.", "weaknesses": "1.\tEvaluation fairness: The paper includes G‑HANet and LD‑CVAE (g.+h.→h) baselines that also aim to impute genomics. However, it is not clear whether they are allowed to retrieve/use training-case genomics at inference in the same way. If not, MSRL enjoys an extra source of information that makes comparisons less equitable. \n2.\tAll results are intra TCGA: an external‑cohort generalization test is highly valuable. This is especially important because MSRL leverages structural similarity and retrieval. Generalization across centers is precisely where structural priors may shift.\n3.\tReport performance when only a fraction (e.g., 10–50\\%) of training cases have genomics (partial-paired training) and when the buffer is limited.", "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)