mlsanityData to 14 Oct 2026

DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction

arXiv 2510.00053 ↗First posted 28 Sep 2025eess.IV cs.CV cs.LGcited by 6

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Abstract (arXiv)

Pathology whole-slide images (WSIs) are widely used for cancer survival analysis because of their comprehensive histopathological information at both cellular and tissue levels, enabling quantitative, large-scale, and prognostically rich tumor feature analysis. However, most existing methods in WSI survival analysis struggle with limited interpretability and often overlook predictive uncertainty in heterogeneous slide images. In this paper, we propose DPsurv, a dual-prototype whole-slide image evidential fusion network that outputs uncertainty-aware survival intervals, while enabling interpretation of predictions through patch prototype assignment maps, component prototypes, and component-wise relative risk aggregation. Experiments on five publicly available datasets achieve the highest mean concordance index and the lowest mean integrated Brier score, validating the effectiveness and reliability of DPsurv. The interpretation of prediction results provides transparency at the feature, reasoning, and decision levels, thereby enhancing the trustworthiness and interpretability of DPsurv.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 33 of its 64 references that are in the map. First posted on arXiv 28 Sep 2025.

Builds on

33 of 64 references.

References · 33

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  10. 2020Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networkscited by 178
  11. 2023Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Imagescited by 134
  12. 2024MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathologycited by 88
21 more
  1. 2023Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Predictioncited by 86
  2. 2024Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathologycited by 72
  3. 2024Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysiscited by 67
  4. 2019Deep Learning for Whole Slide Image Analysis: An Overviewcited by 51
  5. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  6. 2022Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathologycited by 37
  7. 2022Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Imagescited by 32
  8. 2025ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classificationcited by 28
  9. 2025CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Modelcited by 28
  10. 2023SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathologycited by 25
  11. 2024Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathologycited by 14
  12. 2024Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysiscited by 13
  13. 2023Cross-scale Multi-instance Learning for Pathological Image Diagnosiscited by 11
  14. 2022Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomicscited by 8
  15. 2024Evidential time-to-event prediction with calibrated uncertainty quantificationcited by 7
  16. 2020Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidencecited by 6
  17. 2022Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: general framework and practical modelscited by 6
  18. 2024An evidential time-to-event prediction model based on Gaussian random fuzzy numberscited by 5
  19. 2024Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological Imagescited by 5
  20. 2024ST-USleepNet: A Spatial-Temporal Coupling Prominence Network for Multi-Channel Sleep Stagingcited by 4
  21. 2024EsurvFusion: An evidential multimodal survival fusion model based on Gaussian random fuzzy numberscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026Sparse Task Vector Mixup with Hypernetworks for Efficient Knowledge Transfer in Whole-Slide Image Prognosiscited by 2
  2. 2026Bridging the Modality Bottleneck in Pathology MIL through Virtual Molecular Stainingcited by 2
  3. 2026Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysiscited by 0
  4. 2026Evidential Fusion Network for Multimodal Survival Prediction under Missing Modalitiescited by 0
  5. 2026A Multi-Cohort Validation of Censoring-Aware Conformal Lower Predictive Bounds for Pathology Survival Modelscited by 0
  6. 2026CACSurv: Concordance-Aligned Comparative Learning with Large Language Models for Cancer Survival Predictioncited by 0