mlsanityData to 7 Oct 2026

ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification

arXiv 2108.10612 ↗First posted 24 Aug 2021cs.LG cs.AI cs.CVcited by 22

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Abstract (arXiv)

Multiple Instance Learning (MIL) gains popularity in many real-life machine learning applications due to its weakly supervised nature. However, the corresponding effort on explaining MIL lags behind, and it is usually limited to presenting instances of a bag that are crucial for a particular prediction. In this paper, we fill this gap by introducing ProtoMIL, a novel self-explainable MIL method inspired by the case-based reasoning process that operates on visual prototypes. Thanks to incorporating prototypical features into objects description, ProtoMIL unprecedentedly joins the model accuracy and fine-grained interpretability, which we present with the experiments on five recognized MIL datasets.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 24 of its 47 references that are in the map. First posted on arXiv 24 Aug 2021.

Builds on

24 of 47 references.

References · 24

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  3. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,309
  4. 2018Sanity Checks for Saliency Mapscited by 1,149
  5. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  6. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 738
  7. 2018This Looks Like That: Deep Learning for Interpretable Image Recognitioncited by 731
  8. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 549
  9. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 473
  10. 2019On Completeness-aware Concept-Based Explanations in Deep Neural Networkscited by 239
  11. 2017Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictionscited by 234
  12. 2020Concept Whitening for Interpretable Image Recognitioncited by 225
12 more
  1. 2020Neural Prototype Trees for Interpretable Fine-grained Image Recognitioncited by 213
  2. 2019One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniquescited by 164
  3. 2020Self supervised contrastive learning for digital histopathologycited by 146
  4. 2019Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Groundedcited by 127
  5. 2016Revisiting Multiple Instance Neural Networkscited by 119
  6. 2021XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanationscited by 72
  7. 2021IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammographycited by 72
  8. 2021This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networkscited by 65
  9. 2019Interpretable Image Recognition with Hierarchical Prototypescited by 64
  10. 2020There and Back Again: Revisiting Backpropagation Saliency Methodscited by 60
  11. 2020This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognitioncited by 50
  12. 2019Multiple instance learning with graph neural networkscited by 25

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Cited by year (2026 to 7 Oct)

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

22 papers.

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  1. 2023Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanationscited by 27
  2. 2023SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathologycited by 25
  3. 2023Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Needcited by 21
  4. 2024LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Visioncited by 15
  5. 2024xMIL: Insightful Explanations for Multiple Instance Learning in Histopathologycited by 15
  6. 2023Interpreting and Correcting Medical Image Classification with PIP-Netcited by 11
  7. 2024A survey on Concept-based Approaches For Model Improvementcited by 8
  8. 2023Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learningcited by 7
  9. 2023ProMIL: Probabilistic Multiple Instance Learning for Medical Imagingcited by 3
  10. 2025This looks like what? Challenges and Future Research Directions for Part-Prototype Modelscited by 3
  11. 2025Birds look like cars: Adversarial analysis of intrinsically interpretable deep learningcited by 3
  12. 2024Position: Do Not Explain Vision Models Without Contextcited by 2
10 more
  1. 2024Deep spatial context: when attention-based models meet spatial regressioncited by 1
  2. 2024ProtoP-OD: Explainable Object Detection with Prototypical Partscited by 1
  3. 2025Histomorphology-Guided Prototypical Multi-Instance Learning for Breast Cancer WSI Classificationcited by 0
  4. 2025A Prototype-Guided Coarse Annotations Refining Approach for Whole Slide Imagescited by 0
  5. 2025PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysiscited by 0
  6. 2025Diagnose Like A REAL Pathologist: An Uncertainty-Focused Approach for Trustworthy Multi-Resolution Multiple Instance Learningcited by 0
  7. 2026ProtoQuant: Quantization of Prototypical Parts For General and Fine-Grained Image Classificationcited by 0
  8. 2026ProtoPathway: Biologically Structured Prototype-Pathway Fusion for Multimodal Cancer Survival Predictioncited by 0
  9. 2026Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathologycited by 0
  10. 2026Diverse by Design: Architectural Constraints for Prototype-Based Interpretabilitycited by 0