mlsanityData to 14 Oct 2026

Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach

arXiv 1802.02212 ↗First posted 1 Feb 2018cs.CV cs.LG stat.MLcited by 33

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Abstract (arXiv)

Analysis of histopathology slides is a critical step for many diagnoses, and in particular in oncology where it defines the gold standard. In the case of digital histopathological analysis, highly trained pathologists must review vast whole-slide-images of extreme digital resolution (100,0002 pixels) across multiple zoom levels in order to locate abnormal regions of cells, or in some cases single cells, out of millions. The application of deep learning to this problem is hampered not only by small sample sizes, as typical datasets contain only a few hundred samples, but also by the generation of ground-truth localized annotations for training interpretable classification and segmentation models. We propose a method for disease localization in the context of weakly supervised learning, where only image-level labels are available during training. Even without pixel-level annotations, we are able to demonstrate performance comparable with models trained with strong annotations on the Camelyon-16 lymph node metastases detection challenge. We accomplish this through the use of pre-trained deep convolutional networks, feature embedding, as well as learning via top instances and negative evidence, a multiple instance learning technique from the field of semantic segmentation and object detection.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Feb 2018.

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Citing papers per year: 2021: 8; 2022: 7; 2023: 5; 2024: 6; 2025: 6; 2026: 1.

33 papers.

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  1. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  2. 2021A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approachescited by 44
  3. 2021Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathologycited by 25
  4. 2021Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancerscited by 23
  5. 2022Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learningcited by 22
  6. 2022Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-trainingcited by 21
  7. 2021Efficient Classification of Very Large Images with Tiny Objectscited by 17
  8. 2021Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Imagescited by 13
  9. 2023CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Imagescited by 13
  10. 2021WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Imagescited by 9
  11. 2024A Systematic Review of Generalization Research in Medical Image Classificationcited by 8
  12. 2024PathoTune: Adapting Visual Foundation Model to Pathological Specialistscited by 8
21 more
  1. 2021A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformerscited by 7
  2. 2021Gigapixel Histopathological Image Analysis using Attention-based Neural Networkscited by 6
  3. 2022Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA predictive performance with fewer data using Swin Transformercited by 6
  4. 2022Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labelscited by 6
  5. 2023Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide imagescited by 5
  6. 2023Cross-attention-based saliency inference for predicting cancer metastasis on whole slide imagescited by 5
  7. 2022A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterationscited by 4
  8. 2023SRATTA : Sample Re-ATTribution Attack of Secure Aggregation in Federated Learningcited by 3
  9. 2024CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H&E stained imagescited by 3
  10. 2022Colorectal cancer survival prediction using deep distribution based multiple-instance learningcited by 1
  11. 2024INSIGHT: Explainable Weakly-Supervised Medical Image Analysiscited by 1
  12. 2025IMILIA: interpretable multiple instance learning for inflammation prediction in IBD from H&E whole slide imagescited by 1
  13. 2023MesoGraph: Automatic Profiling of Malignant Mesothelioma Subtypes from Histological Imagescited by 0
  14. 2024CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Imagescited by 0
  15. 2024Leveraging Transfer Learning and Multiple Instance Learning for HER2 Automatic Scoring of H&E Whole Slide Imagescited by 0
  16. 2025Robust sensitivity control in digital pathology via tile score distribution matchingcited by 0
  17. 2025Leveraging Spatial Context for Positive Pair Sampling in Histopathology Image Representation Learningcited by 0
  18. 2025Histomorphology-Guided Prototypical Multi-Instance Learning for Breast Cancer WSI Classificationcited by 0
  19. 2025Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Imagescited by 0
  20. 2025GAS-MIL: Group-Aggregative Selection Multi-Instance Learning for Ensemble of Foundation Models in Digital Pathology Image Analysiscited by 0
  21. 2026Multimodal Fusion of Histopathology Images and Electronic Health Records for Early Breast Cancer Diagnosiscited by 0