pith. sign in

arxiv: 2606.19966 · v1 · pith:LKH2BKILnew · submitted 2026-06-18 · 💻 cs.CV · cs.LG

Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysis

Yucheng Xing , Ling Huang , Pei Liu , Jingying Ma , Jiaqing Xu , Kai He , Mengling Feng This is my paper

Pith reviewed 2026-06-26 18:32 UTC · model grok-4.3

classification 💻 cs.CV cs.LG
keywords whole-slide imagessurvival analysisdomain generalizationvisual question answeringevidential fusionpathology semanticszero-shot transferuncertainty modeling
0
0 comments X

The pith

High-level semantic anchors from visual questions let survival models trained on one hospital generalize to others without retraining.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that pixel-derived features in whole-slide images shift too much across clinical centers due to staining and scanner differences, breaking model performance on new sites. It proposes instead deriving stable semantic anchors via visual question answering on concepts like tumor grade and tissue architecture, then fusing those anchors with visual evidence streams using Dirichlet-based subjective logic and a cautious conjunction rule. This produces survival predictions that hold up when the model sees only one training domain and is tested zero-shot on four unseen domains. The result is a 10.2 percent average gain in C-index plus lower cross-center divergence in the semantic features themselves. A sympathetic reader would care because the approach aims to let hospitals adopt the same model without collecting new labeled whole-slide images each time.

Core claim

SAEFS derives semantic anchors from WSIs via Visual Question Answering, runs a dual-stream evidence extractor, models uncertainty with Dirichlet-based Subjective Logic, and fuses the semantic and visual evidence streams through a cautious conjunction rule, yielding survival estimates that remain accurate and reliable when trained on a single source domain and evaluated zero-shot on four unseen domains.

What carries the argument

Semantic anchors obtained via Visual Question Answering on pathology concepts, fused with visual evidence through cautious conjunction in Subjective Logic.

Load-bearing premise

High-level pathology semantics such as tumor grade and micro-environmental architecture remain consistent across different staining protocols and scanners.

What would settle it

Measurements showing that VQA-derived semantic features exhibit high cross-center divergence or that the fused model fails to improve C-index on the four unseen domains.

Figures

Figures reproduced from arXiv: 2606.19966 by Jiaqing Xu, Jingying Ma, Kai He, Ling Huang, Mengling Feng, Pei Liu, Yucheng Xing.

Figure 1
Figure 1. Figure 1: Domain-shift robustness analysis for lung adenocarcinoma under TCGA to NLST transfer. (a) C-index of ABMIL [19], TransMIL [44], and ILRA [54] on the source domain (TCGA) and target domain (NLST), where ∆ denotes the performance drop after transfer. (b) t-SNE visualization of cross-domain WSI features. Maximum Mean Discrepancy (MMD) values in (b) quantify the domain discrepancy in each feature space. and le… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed framework. Template-based VQA answers serve as semantic anchors to extract text-guided visual evidence from the WSI. A parallel WSI-only branch provides complementary visual evidence. The two visual evidence streams are adaptively mixed, then fused with pure text evidence through Dirichlet￾based cautious belief fusion that preserves uncertainty under modality disagreement multiple-… view at source ↗
Figure 3
Figure 3. Figure 3: Cross-center feature distributions under domain shift. [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Kaplan–Meier risk stratification curves on external datasets (TCGA → CP￾TAC/NLST). High- and low-risk groups are separated by the median predicted risk. (a) CPTAC-KIRC (b) CPTAC-LUAD (c) CPTAC-UCEC (d) NLST-LUAD [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Calibration plots comparing predicted and observed survival probabilities on external cohorts (TCGA → CPTAC/NLST). Curves closer to the diagonal indicate better calibration of survival risk predictions. SAEFS consistently produces curves closer to the ideal diagonal, indicating im￾proved calibration prediction under domain shift. Notably, although several mul￾timodal baselines achieve slightly lower IBS an… view at source ↗
Figure 6
Figure 6. Figure 6: 7-cohort slide feature distribution visualized by t-SNE [PITH_FULL_IMAGE:figures/full_fig_p023_6.png] view at source ↗
read the original abstract

Whole-slide images (WSIs) are widely used for computational cancer prognosis. However, most existing methods primarily focus on in-domain performance and fail to generalize across clinical centers. This limitation stems from their reliance on pixel-derived representations that are highly susceptible to domain-specific artifacts caused by staining protocols and scanner hardware. We hypothesize that high-level pathology semantics, such as tumor grade and micro-environmental architecture, provide a domain-invariant semantic representation that mirrors the robust diagnostic logic of human pathologists. Therefore, we propose a Semantic-Anchored Evidential Fusion Survival (SAEFS) framework, where SAEFS derives semantic anchors from WSIs via Visual Question Answering (VQA), employs a dual-stream WSI evidence extraction architecture, uses Dirichlet-based Subjective Logic to model uncertainty, and fuses semantic and visual evidence through a cautious conjunction rule to avoid overconfident fusion from correlated sources. Trained exclusively on one source domain and evaluated zero-shot across four unseen domains, SAEFS consistently outperforms state-of-the-art models both in prediction accuracy and reliability, improving the average C-index by 10.2%. Quantitative analyses further show that VQA-derived semantic features exhibit significantly lower cross-center divergence than pixel-derived features, highlighting their robustness for cross-center clinical applications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes the Semantic-Anchored Evidential Fusion Survival (SAEFS) framework for whole-slide image (WSI) survival analysis. It extracts high-level semantic anchors (tumor grade, micro-environmental architecture) via Visual Question Answering (VQA), employs a dual-stream architecture for visual and semantic evidence, models uncertainty with Dirichlet-based subjective logic, and fuses the streams via a cautious conjunction rule. The method is trained exclusively on a single source domain and evaluated zero-shot on four unseen domains, reporting a 10.2% average C-index gain over state-of-the-art baselines together with lower cross-center feature divergence for the VQA-derived anchors.

Significance. If the domain-invariance of the VQA anchors and the attribution of the performance gain can be rigorously established, the work would address a practically important limitation in computational pathology—domain shift due to staining and scanner variation—without requiring multi-center training data. The explicit uncertainty modeling via subjective logic is a methodological strength that could improve reliability assessments in clinical settings.

major comments (3)
  1. [Experiments / Ablation studies] The central claim that VQA-derived semantic anchors remain domain-invariant (and drive the 10.2% C-index gain) is load-bearing, yet the manuscript provides no ablation that replaces the VQA anchors with purely visual features while retaining the dual-stream architecture, Dirichlet fusion, and cautious conjunction rule. Without this control, the performance improvement cannot be attributed to semantic invariance rather than other modeling choices. (Experiments / Ablation studies section)
  2. [Method, VQA and fusion subsections] The invariance hypothesis requires that the VQA component itself does not encode source-domain visual cues. The manuscript does not state whether the VQA model is a frozen off-the-shelf network or fine-tuned on the source-domain WSIs, nor does it report the VQA architecture, its pre-training corpus, or any domain-shift experiments on the VQA outputs alone. These omissions directly affect whether the reported lower cross-center divergence and zero-shot gains can be credited to the semantic anchors. (Method section, VQA and fusion subsections)
  3. [Results / Quantitative comparison] Table or figure reporting the 10.2% average C-index improvement (and the per-domain results) does not include the number of WSIs per center, the exact survival endpoints, the full list of baselines with their hyper-parameter settings, or statistical significance tests (e.g., paired Wilcoxon or DeLong test on C-index). These details are required to evaluate whether the gain is robust and reproducible. (Results / Quantitative comparison section)
minor comments (2)
  1. [Method] Notation for the Dirichlet concentration parameters and the cautious conjunction rule should be introduced with explicit equations and a short derivation or reference to the subjective-logic literature to improve readability for readers outside the evidential-reasoning community.
  2. [Figures] Figure captions for the cross-center divergence plots should explicitly state the divergence metric (e.g., MMD, Wasserstein) and the feature dimensionality being compared.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important aspects for strengthening the attribution of our results and improving reproducibility. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Experiments / Ablation studies] The central claim that VQA-derived semantic anchors remain domain-invariant (and drive the 10.2% C-index gain) is load-bearing, yet the manuscript provides no ablation that replaces the VQA anchors with purely visual features while retaining the dual-stream architecture, Dirichlet fusion, and cautious conjunction rule. Without this control, the performance improvement cannot be attributed to semantic invariance rather than other modeling choices. (Experiments / Ablation studies section)

    Authors: We agree that this ablation is necessary to rigorously attribute the performance gains to the semantic invariance of the VQA anchors rather than other architectural choices. In the revised manuscript, we will add an ablation study that replaces the VQA-derived semantic stream with additional purely visual features while retaining the dual-stream architecture, Dirichlet-based subjective logic, and cautious conjunction rule. This will directly test whether the 10.2% C-index improvement and reduced cross-center divergence are driven by the semantic anchors. revision: yes

  2. Referee: [Method, VQA and fusion subsections] The invariance hypothesis requires that the VQA component itself does not encode source-domain visual cues. The manuscript does not state whether the VQA model is a frozen off-the-shelf network or fine-tuned on the source-domain WSIs, nor does it report the VQA architecture, its pre-training corpus, or any domain-shift experiments on the VQA outputs alone. These omissions directly affect whether the reported lower cross-center divergence and zero-shot gains can be credited to the semantic anchors. (Method section, VQA and fusion subsections)

    Authors: We acknowledge these details were omitted. The VQA model is a frozen off-the-shelf network not fine-tuned on source-domain WSIs; we will explicitly state this in the revised Method section along with the specific architecture, pre-training corpus, and any relevant hyperparameters. Additionally, we will include domain-shift experiments on the VQA outputs alone (e.g., cross-center divergence metrics and zero-shot performance of VQA features) to support the invariance claim. revision: yes

  3. Referee: [Results / Quantitative comparison] Table or figure reporting the 10.2% average C-index improvement (and the per-domain results) does not include the number of WSIs per center, the exact survival endpoints, the full list of baselines with their hyper-parameter settings, or statistical significance tests (e.g., paired Wilcoxon or DeLong test on C-index). These details are required to evaluate whether the gain is robust and reproducible. (Results / Quantitative comparison section)

    Authors: We agree these details are essential for reproducibility and assessing robustness. In the revised Results section, we will expand the relevant table/figure to report the number of WSIs per center, exact survival endpoints used, the complete list of baselines with their hyper-parameter settings, and statistical significance tests (paired Wilcoxon signed-rank tests on C-index values across domains). revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical zero-shot claims are externally testable.

full rationale

The paper advances an empirical framework (SAEFS) with training restricted to one source domain and zero-shot evaluation on four unseen domains, reporting a 10.2% average C-index gain. No equations, fitted parameters, or self-citations are presented that reduce the reported performance or the claimed domain-invariance of VQA anchors to quantities defined by construction within the same work. The hypothesis that high-level semantics are domain-invariant is stated as motivation and supported by post-hoc divergence measurements, but these measurements are independent of the fusion rules and do not create a definitional loop. The derivation chain therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on the unverified assumption that VQA outputs remain domain-invariant and that the cautious fusion rule correctly handles source correlation; multiple implementation parameters in the evidential model are unspecified and therefore implicitly fitted.

free parameters (2)
  • Dirichlet concentration parameters
    Parameters controlling uncertainty modeling in subjective logic are not stated and must be chosen or fitted.
  • Cautious conjunction rule parameters
    Tuning parameters for the fusion operator are not described.
axioms (2)
  • domain assumption VQA-derived semantics are domain-invariant across staining and scanner variations
    Invoked as the core hypothesis enabling zero-shot transfer.
  • domain assumption Semantic and visual evidence streams are correlated yet the cautious rule prevents overconfident fusion
    Required for the claimed reliability improvement.

pith-pipeline@v0.9.1-grok · 5768 in / 1303 out tokens · 40060 ms · 2026-06-26T18:32:46.565034+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

60 extracted references · 3 linked inside Pith

  1. [1]

    Nature medicine30(3), 850–862 (2024)

    Chen, R.J., Ding, T., Lu, M.Y., Williamson, D.F., Jaume, G., Song, A.H., Chen, B., Zhang, A., Shao, D., Shaban, M., et al.: Towards a general-purpose foundation model for computational pathology. Nature medicine30(3), 850–862 (2024)

    2024

  2. [2]

    In: Proceedings of the IEEE/CVF interna- tional conference on computer vision

    Chen, R.J., Lu, M.Y., Weng, W.H., Chen, T.Y., Williamson, D.F., Manz, T., Shady, M., Mahmood, F.: Multimodal co-attention transformer for survival pre- diction in gigapixel whole slide images. In: Proceedings of the IEEE/CVF interna- tional conference on computer vision. pp. 4015–4025 (2021)

    2021

  3. [3]

    Cancer cell40(8), 865–878 (2022)

    Chen, R.J., Lu, M.Y., Williamson, D.F., Chen, T.Y., Lipkova, J., Noor, Z., Shaban, M., Shady, M., Williams, M., Joo, B., et al.: Pan-cancer integrative histology- genomic analysis via multimodal deep learning. Cancer cell40(8), 865–878 (2022)

    2022

  4. [4]

    In: 11th International Conference on Learning Representations (ICLR 2023) (2023)

    Cui, Y., Liu, Z., Liu, X., Liu, X., Wang, C., Kuo, T.W., Xue, C.J., Chan, A.B.: Bayes-mil: A new probabilistic perspective on attention-based multiple instance learning for whole slide images. In: 11th International Conference on Learning Representations (ICLR 2023) (2023)

    2023

  5. [5]

    In: Classic works of the Dempster-Shafer theory of belief functions, pp

    Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. In: Classic works of the Dempster-Shafer theory of belief functions, pp. 57–72. Springer (2008)

    2008

  6. [6]

    Artificial intelligence172(2-3), 234–264 (2008)

    Denœux, T.: Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence. Artificial intelligence172(2-3), 234–264 (2008)

    2008

  7. [7]

    Journal of proteome research14(6), 2707–2713 (2015)

    Edwards, N.J., Oberti, M., Thangudu, R.R., Cai, S., McGarvey, P.B., Jacob, S., Madhavan, S., Ketchum, K.A.: The cptac data portal: a resource for cancer pro- teomics research. Journal of proteome research14(6), 2707–2713 (2015)

    2015

  8. [8]

    MedRxiv pp

    Filiot, A., Ghermi, R., Olivier, A., Jacob, P., Fidon, L., Camara, A., Mac Kain, A., Saillard, C., Schiratti, J.B.: Scaling self-supervised learning for histopathology with masked image modeling. MedRxiv pp. 2023–07 (2023)

    2023

  9. [9]

    In: international conference on machine learn- ing

    Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learn- ing. pp. 1050–1059. PMLR (2016)

    2016

  10. [10]

    medRxiv pp

    Gao, Z., He, K., Su, W., Machado, I.P., McGough, W., Jimenez-Linan, M., Rous, B., Wang, C., Li, C., Pang, X., et al.: Alpaca: Adapting llama for pathology context analysis to enable slide-level question answering. medRxiv pp. 2025–04 (2025)

    2025

  11. [11]

    The journal of machine learning research13(1), 723–773 (2012)

    Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A.: A kernel two-sample test. The journal of machine learning research13(1), 723–773 (2012)

    2012

  12. [12]

    IEEE transactions on pattern analysis and machine intelligence45(2), 2551–2566 (2022)

    Han, Z., Zhang, C., Fu, H., Zhou, J.T.: Trusted multi-view classification with dynamic evidential fusion. IEEE transactions on pattern analysis and machine intelligence45(2), 2551–2566 (2022)

    2022

  13. [13]

    Medical Image Analysis97, 103223 (2024)

    Huang, L., Ruan, S., Xing, Y., Feng, M.: A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods. Medical Image Analysis97, 103223 (2024)

    2024

  14. [14]

    In: International Conference on Belief Functions

    Huang, L., Xing, Y., Denoeux, T., Feng, M.: An evidential time-to-event prediction model based on gaussian random fuzzy numbers. In: International Conference on Belief Functions. pp. 49–57. Springer (2024)

    2024

  15. [15]

    IEEE Transactions on Fuzzy Systems (2025)

    Huang, L., Xing, Y., Lin, Q., Duan, J., Ruan, S., Feng, M.: Esurvfusion: An ev- idential multimodal survival fusion model based on epistemic random fuzzy sets. IEEE Transactions on Fuzzy Systems (2025)

    2025

  16. [16]

    International Journal of Ap- proximate Reasoning181, 109403 (2025) Semantic-Anchored Evidential Fusion 17

    Huang, L., Xing, Y., Mishra, S., Denœux, T., Feng, M.: Evidential time-to-event prediction with calibrated uncertainty quantification. International Journal of Ap- proximate Reasoning181, 109403 (2025) Semantic-Anchored Evidential Fusion 17

    2025

  17. [17]

    Nature medicine29(9), 2307–2316 (2023)

    Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T.J., Zou, J.: A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine29(9), 2307–2316 (2023)

    2023

  18. [18]

    Advances in neural information processing systems36, 37995– 38017 (2023)

    Ikezogwo, W., Seyfioglu, S., Ghezloo, F., Geva, D., Sheikh Mohammed, F., Anand, P.K., Krishna, R., Shapiro, L.: Quilt-1m: One million image-text pairs for histopathology. Advances in neural information processing systems36, 37995– 38017 (2023)

    2023

  19. [19]

    In: International conference on machine learning

    Ilse,M.,Tomczak,J.,Welling,M.:Attention-baseddeepmultipleinstancelearning. In: International conference on machine learning. pp. 2127–2136. PMLR (2018)

    2018

  20. [20]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    Jaume, G., Vaidya, A., Chen, R., Williamson, D., Liang, P., Mahmood, F.: Mod- eling dense multimodal interactions between biological pathways and histology for survival prediction. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  21. [21]

    IEEE Transactions on Medical Imaging (2025)

    Jiang, S., Cai, L., Gan, Z., Wang, Y., Tang, G., Zhang, Y.: Uncertainty-aware survival analysis with dirichlet distribution for multi-scale pathology and genomics. IEEE Transactions on Medical Imaging (2025)

    2025

  22. [22]

    arXiv preprint arXiv:2501.18055 (2025)

    de Jong, E.D., Marcus, E., Teuwen, J.: Current pathology foundation models are unrobust to medical center differences. arXiv preprint arXiv:2501.18055 (2025)

    arXiv 2025

  23. [23]

    Springer Publishing Company, Incorporated (2018)

    Jsang, A.: Subjective Logic: A formalism for reasoning under uncertainty. Springer Publishing Company, Incorporated (2018)

    2018

  24. [24]

    Journal of digital imaging33(4), 1034–1040 (2020)

    Kumar, N., Gupta, R., Gupta, S.: Whole slide imaging (wsi) in pathology: current perspectives and future directions. Journal of digital imaging33(4), 1034–1040 (2020)

    2020

  25. [25]

    Advances in neural information pro- cessing systems30(2017)

    Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information pro- cessing systems30(2017)

    2017

  26. [26]

    In: Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Li, B., Li, Y., Eliceiri, K.W.: Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 14318–14328 (2021)

    2021

  27. [27]

    Advances in Neural Information Processing Systems36, 28541–28564 (2023)

    Li, C., Wong, C., Zhang, S., Usuyama, N., Liu, H., Yang, J., Naumann, T., Poon, H., Gao, J.: Llava-med: Training a large language-and-vision assistant for biomedicine in one day. Advances in Neural Information Processing Systems36, 28541–28564 (2023)

    2023

  28. [28]

    International Journal of Computer Vision133(1), 31–64 (2025)

    Liang, J., He, R., Tan, T.: A comprehensive survey on test-time adaptation under distribution shifts. International Journal of Computer Vision133(1), 31–64 (2025)

    2025

  29. [29]

    In: International conference on machine learning

    Liang, J., Hu, D., Feng, J.: Do we really need to access the source data? source hy- pothesis transfer for unsupervised domain adaptation. In: International conference on machine learning. pp. 6028–6039. PMLR (2020)

    2020

  30. [30]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Liu,J.,Li,H.,Yang,C.,Deutges,M.,Sadafi,A.,You,X.,Breininger,K.,Navab,N., Schüffler, P.J.: Hasd: hierarchical adaption for pathology slide-level domain-shift. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 332–342. Springer (2025)

    2025

  31. [31]

    Nature Medicine30, 863–874 (2024)

    Lu, M.Y., Chen, B., Williamson, D.F., Chen, R.J., Liang, I., Ding, T., Jaume, G., Odintsov, I., Le, L.P., Gerber, G., et al.: A visual-language foundation model for computational pathology. Nature Medicine30, 863–874 (2024)

    2024

  32. [32]

    Nature634(8033), 466–473 (2024) 18 Y

    Lu, M.Y., Chen, B., Williamson, D.F., Chen, R.J., Zhao, M., Chow, A.K., Ikemura, K., Kim, A., Pouli, D., Patel, A., et al.: A multimodal generative ai copilot for human pathology. Nature634(8033), 466–473 (2024) 18 Y. Xing et al

    2024

  33. [33]

    In: Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition

    Lu, M.Y., Chen, B., Zhang, A., Williamson, D.F., Chen, R.J., Ding, T., Le, L.P., Chuang, Y.S., Mahmood, F.: Visual language pretrained multiple instance zero- shot transfer for histopathology images. In: Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition. pp. 19764–19775 (2023)

    2023

  34. [34]

    Nature biomedical engineering5(6), 555–570 (2021)

    Lu, M.Y., Williamson, D.F., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nature biomedical engineering5(6), 555–570 (2021)

    2021

  35. [35]

    In: 2009 IEEE international symposium on biomedical imaging: from nano to macro

    Macenko, M., Niethammer, M., Marron, J.S., Borland, D., Woosley, J.T., Guan, X., Schmitt, C., Thomas, N.E.: A method for normalizing histology slides for quan- titative analysis. In: 2009 IEEE international symposium on biomedical imaging: from nano to macro. pp. 1107–1110. IEEE (2009)

    2009

  36. [36]

    Proceedings of the National Academy of Sciences115(13), E2970–E2979 (2018)

    Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D.A., Barnholtz-Sloan, J.S., Velázquez Vega, J.E., Brat, D.J., Cooper, L.A.: Predicting cancer outcomes from histology and genomics using convolutional networks. Proceedings of the National Academy of Sciences115(13), E2970–E2979 (2018)

    2018

  37. [37]

    In: International conference on machine learning

    Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PmLR (2021)

    2021

  38. [38]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Raza, M., Azam, A., Qaiser, T., Rajpoot, N.: Ps3: A multimodal transformer inte- grating pathology reports with histology images and biological pathways for cancer survival prediction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 22175–22186 (2025)

    2025

  39. [39]

    IEEE Computer graphics and applications21(5), 34–41 (2002)

    Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between im- ages. IEEE Computer graphics and applications21(5), 34–41 (2002)

    2002

  40. [40]

    In: International Conference on Medical Image Computing and Computer- Assisted Intervention

    Ren, Q., Wang, Y., Fang, R., Ling, H., You, C.: Otsurv: A novel multiple instance learning framework for survival prediction with heterogeneity-aware optimal trans- port. In: International Conference on Medical Image Computing and Computer- Assisted Intervention. pp. 439–449. Springer (2025)

    2025

  41. [41]

    Cell reports23(1), 181–193 (2018)

    Saltz,J.,Gupta,R.,Hou,L.,Kurc,T.,Singh,P.,Nguyen,V.,Samaras,D.,Shroyer, K.R., Zhao, T., Batiste, R., et al.: Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell reports23(1), 181–193 (2018)

    2018

  42. [42]

    Advances in neural information processing systems31(2018)

    Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classi- fication uncertainty. Advances in neural information processing systems31(2018)

    2018

  43. [43]

    Princeton university press (2020)

    Shafer, G.: A mathematical theory of evidence. Princeton university press (2020)

    2020

  44. [44]

    Advances in neural information processing systems34, 2136–2147 (2021)

    Shao, Z., Bian, H., Chen, Y., Wang, Y., Zhang, J., Ji, X., et al.: Transmil: Trans- former based correlated multiple instance learning for whole slide image classifica- tion. Advances in neural information processing systems34, 2136–2147 (2021)

    2021

  45. [45]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Shou, Y., Cao, X., Yan, P., Hui, Q., Zhao, Q., Meng, D.: Graph domain adaptation with dual-branch encoder and two-level alignment for whole slide image-based survival prediction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 19925–19935 (2025)

    2025

  46. [46]

    Nature Reviews Bioengineering1(12), 930–949 (2023)

    Song, A.H., Jaume, G., Williamson, D.F., Lu, M.Y., Vaidya, A., Miller, T.R., Mah- mood, F.: Artificial intelligence for digital and computational pathology. Nature Reviews Bioengineering1(12), 930–949 (2023)

    2023

  47. [47]

    New England Journal of Medicine365(5), 395–409 (2011)

    Team, N.L.S.T.R.: Reduced lung-cancer mortality with low-dose computed tomo- graphic screening. New England Journal of Medicine365(5), 395–409 (2011)

    2011

  48. [48]

    Medical image analysis58, 101544 (2019)

    Tellez, D., Litjens, G., Bándi, P., Bulten, W., Bokhorst, J.M., Ciompi, F., Van Der Laak, J.: Quantifying the effects of data augmentation and stain color nor- Semantic-Anchored Evidential Fusion 19 malization in convolutional neural networks for computational pathology. Medical image analysis58, 101544 (2019)

    2019

  49. [49]

    Contemporary Oncol- ogy/Współczesna Onkologia2015(1), 68–77 (2015)

    Tomczak, K., Czerwińska, P., Wiznerowicz, M.: Review the cancer genome atlas (tcga): an immeasurable source of knowledge. Contemporary Oncol- ogy/Współczesna Onkologia2015(1), 68–77 (2015)

    2015

  50. [50]

    arXiv preprint arXiv:2302.13971 (2023)

    Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)

    Pith/arXiv arXiv 2023

  51. [51]

    IEEE transactions on medical imaging35(8), 1962–1971 (2016)

    Vahadane, A., Peng, T., Sethi, A., Albarqouni, S., Wang, L., Baust, M., Steiger, K., Schlitter, A.M., Esposito, I., Navab, N.: Structure-preserving color normalization and sparse stain separation for histological images. IEEE transactions on medical imaging35(8), 1962–1971 (2016)

    1962

  52. [52]

    arXiv preprint arXiv:2006.10726 (2020)

    Wang, D., Shelhamer, E., Liu, S., Olshausen, B., Darrell, T.: Tent: Fully test-time adaptation by entropy minimization. arXiv preprint arXiv:2006.10726 (2020)

    Pith/arXiv arXiv 2006

  53. [53]

    Medical image analysis81, 102559 (2022)

    Wang, X., Yang, S., Zhang, J., Wang, M., Zhang, J., Yang, W., Huang, J., Han, X.: Transformer-based unsupervised contrastive learning for histopathological image classification. Medical image analysis81, 102559 (2022)

    2022

  54. [54]

    In: The Eleventh International Conference on Learning Representations (2023)

    Xiang, J., Zhang, J.: Exploring low-rank property in multiple instance learning for whole slide image classification. In: The Eleventh International Conference on Learning Representations (2023)

    2023

  55. [55]

    arXiv preprint arXiv:2510.00053 (2025)

    Xing, Y., Huang, L., Ma, J., Hong, R., Qiu, J., Liu, P., He, K., Fu, H., Feng, M.: Dpsurv: Dual-prototype evidential fusion for uncertainty-aware and interpretable whole-slide image survival prediction. arXiv preprint arXiv:2510.00053 (2025)

    Pith/arXiv arXiv 2025

  56. [56]

    In: Proceedings of the IEEE/CVF international conference on computer vision

    Xu, Y., Chen, H.: 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. 21241–21251 (2023)

    2023

  57. [57]

    In: Proceedings of the IEEE/CVF international conference on computer vision

    Yang, S., Wang, Y., Van De Weijer, J., Herranz, L., Jui, S.: Generalized source-free domain adaptation. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 8978–8987 (2021)

    2021

  58. [58]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Zhang, H., Meng, Y., Zhao, Y., Qiao, Y., Yang, X., Coupland, S.E., Zheng, Y.: Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathol- ogy whole slide image classification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 18802–18812 (2022)

    2022

  59. [59]

    In: European conference on computer vision

    Zhang, Y., Li, H., Sun, Y., Zheng, S., Zhu, C., Yang, L.: Attention-challenging mul- tiple instance learning for whole slide image classification. In: European conference on computer vision. pp. 125–143. Springer (2024)

    2024

  60. [60]

    not assessable in this slide

    Zhu, X., Yao, J., Zhu, F., Huang, J.: Wsisa: Making survival prediction from whole slide histopathological images. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7234–7242 (2017) 20 Y. Xing et al. 6 Appendix 6.1 Semantic Anchor Generation via Slide-Level VQA Slide-Level Vision-Language ModelTo generate semantic anch...

    2017