mlsanityData to 14 Oct 2026

AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images

arXiv 2212.06515 ↗First posted 13 Dec 2022eess.IV cs.CVcited by 24

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Abstract (arXiv)

The survival analysis on histological whole-slide images (WSIs) is one of the most important means to estimate patient prognosis. Although many weakly-supervised deep learning models have been developed for gigapixel WSIs, their potential is generally restricted by classical survival analysis rules and fully-supervised learning requirements. As a result, these models provide patients only with a completely-certain point estimation of time-to-event, and they could only learn from the labeled WSI data currently at a small scale. To tackle these problems, we propose a novel adversarial multiple instance learning (AdvMIL) framework. This framework is based on adversarial time-to-event modeling, and integrates the multiple instance learning (MIL) that is much necessary for WSI representation learning. It is a plug-and-play one, so that most existing MIL-based end-to-end methods can be easily upgraded by applying this framework, gaining the improved abilities of survival distribution estimation and semi-supervised learning. Our extensive experiments show that AdvMIL not only could often bring performance improvement to mainstream WSI survival analysis methods at a relatively low computational cost, but also enables these methods to effectively utilize unlabeled data via semi-supervised learning. Moreover, it is observed that AdvMIL could help improving the robustness of models against patch occlusion and two representative image noises. The proposed AdvMIL framework could promote the research of survival analysis in computational pathology with its novel adversarial MIL paradigm.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 29 of its 66 references that are in the map. First posted on arXiv 13 Dec 2022.

Builds on

29 of 66 references.

References · 29

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  12. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
17 more
  1. 2021Intriguing Properties of Vision Transformerscited by 446
  2. 2019Unlabeled Data Improves Adversarial Robustnesscited by 416
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  13. 2018Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networkscited by 57
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 6; 2025: 12; 2026: 2.

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  1. 2024Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Predictioncited by 41
  2. 2023Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classificationcited by 18
  3. 2023Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directionscited by 16
  4. 2024Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathologycited by 14
  5. 2024Tissue Concepts: supervised foundation models in computational pathologycited by 8
  6. 2023ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classificationcited by 5
  7. 2024Weakly-Supervised Residual Evidential Learning for Multi-Instance Uncertainty Estimationcited by 4
  8. 2024Benchmarking Histopathology Foundation Models for Ovarian Cancer Bevacizumab Treatment Response Prediction from Whole Slide Imagescited by 3
  9. 2025HySurvPred: Multimodal Hyperbolic Embedding with Angle-Aware Hierarchical Contrastive Learning and Uncertainty Constraints for Survival Predictioncited by 2
  10. 2025Lifelong Whole Slide Image Analysis: Online Vision-Language Adaptation and Past-to-Present Gradient Distillationcited by 2
  11. 2025Continual Multiple Instance Learning with Enhanced Localization for Histopathological Whole Slide Image Analysiscited by 2
  12. 2025HiLa: Hierarchical Vision-Language Collaboration for Cancer Survival Predictioncited by 2
12 more
  1. 2026Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathologycited by 2
  2. 2026Sparse Task Vector Mixup with Hypernetworks for Efficient Knowledge Transfer in Whole-Slide Image Prognosiscited by 2
  3. 2025ZeroSlide: Is Zero-Shot Classification Adequate for Lifelong Learning in Whole-Slide Image Analysis in the Era of Pathology Vision-Language Foundation Models?cited by 1
  4. 2025Welcome New Doctor: Continual Learning with Expert Consultation and Autoregressive Inference for Whole Slide Image Analysiscited by 1
  5. 2025IPGPhormer: Interpretable Pathology Graph-Transformer for Survival Analysiscited by 1
  6. 2025Cross-Cancer Knowledge Transfer in WSI-based Prognosis Predictioncited by 1
  7. 2023Survival Analysis with Adversarial Regularizationcited by 0
  8. 2024Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Imagescited by 0
  9. 2025A Multi-Modal Deep Learning Framework for Pan-Cancer Prognosiscited by 0
  10. 2025Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Riskscited by 0
  11. 2025Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Imagescited by 0
  12. 2025C2MIL: Synchronizing Semantic and Topological Causalities in Multiple Instance Learning for Robust and Interpretable Survival Analysiscited by 0