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

arXiv: https://arxiv.org/abs/1802.02212 · HTML: https://mlsanity.com/paper/1802.02212/

## Key facts

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

## Built on by (most cited first, 30 of 33)

- [FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings](https://mlsanity.com/paper/2210.04620/) (2022)
- [A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches](https://mlsanity.com/paper/2102.10553/) (2021)
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- [Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers](https://mlsanity.com/paper/2109.05819/) (2021)
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- [Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images](https://mlsanity.com/paper/2109.10778/) (2021)
- [CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images](https://mlsanity.com/paper/2305.05314/) (2023)
- [WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images](https://mlsanity.com/paper/2105.10445/) (2021)
- [A Systematic Review of Generalization Research in Medical Image Classification](https://mlsanity.com/paper/2403.12167/) (2024)
- [PathoTune: Adapting Visual Foundation Model to Pathological Specialists](https://mlsanity.com/paper/2403.16497/) (2024)
- [A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers](https://mlsanity.com/paper/2104.06243/) (2021)
- [Gigapixel Histopathological Image Analysis using Attention-based Neural Networks](https://mlsanity.com/paper/2101.09992/) (2021)
- [Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA predictive performance with fewer data using Swin Transformer](https://mlsanity.com/paper/2208.10495/) (2022)
- [Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labels](https://mlsanity.com/paper/2210.09021/) (2022)
- [Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images](https://mlsanity.com/paper/2301.07700/) (2023)
- [Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images](https://mlsanity.com/paper/2309.09412/) (2023)
- [A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations](https://mlsanity.com/paper/2206.00455/) (2022)
- [SRATTA : Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2306.07644/) (2023)
- [CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H&E stained images](https://mlsanity.com/paper/2401.16131/) (2024)
- [Colorectal cancer survival prediction using deep distribution based multiple-instance learning](https://mlsanity.com/paper/2204.11294/) (2022)
- [INSIGHT: Explainable Weakly-Supervised Medical Image Analysis](https://mlsanity.com/paper/2412.02012/) (2024)
- [IMILIA: interpretable multiple instance learning for inflammation prediction in IBD from H&E whole slide images](https://mlsanity.com/paper/2512.13440/) (2025)
- [MesoGraph: Automatic Profiling of Malignant Mesothelioma Subtypes from Histological Images](https://mlsanity.com/paper/2302.12653/) (2023)
- [CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images](https://mlsanity.com/paper/2408.00427/) (2024)
- [Leveraging Transfer Learning and Multiple Instance Learning for HER2 Automatic Scoring of H&E Whole Slide Images](https://mlsanity.com/paper/2411.05028/) (2024)
- [Robust sensitivity control in digital pathology via tile score distribution matching](https://mlsanity.com/paper/2502.20144/) (2025)
- [Leveraging Spatial Context for Positive Pair Sampling in Histopathology Image Representation Learning](https://mlsanity.com/paper/2503.05170/) (2025)
- [Histomorphology-Guided Prototypical Multi-Instance Learning for Breast Cancer WSI Classification](https://mlsanity.com/paper/2503.17983/) (2025)

## 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.