# CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation

arXiv: https://arxiv.org/abs/1908.10555 · HTML: https://mlsanity.com/paper/1908.10555/

## Key facts

- Cited by 73 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 30 of 73)

- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2203.12081/) (2022)
- [Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images](https://mlsanity.com/paper/2303.06873/) (2023)
- [Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction](https://mlsanity.com/paper/2306.08330/) (2023)
- [Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology](https://mlsanity.com/paper/2402.17228/) (2024)
- [Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis](https://mlsanity.com/paper/2403.07719/) (2024)
- [Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification](https://mlsanity.com/paper/2307.15254/) (2023)
- [CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology](https://mlsanity.com/paper/2412.12077/) (2024)
- [Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis](https://mlsanity.com/paper/2208.08789/) (2022)
- [ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2502.08391/) (2025)
- [Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction](https://mlsanity.com/paper/2402.19326/) (2024)
- [DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2407.03575/) (2024)
- [Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs](https://mlsanity.com/paper/2103.03129/) (2021)
- [Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning](https://mlsanity.com/paper/2210.09452/) (2022)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images](https://mlsanity.com/paper/2206.06665/) (2022)
- [HVTSurv: Hierarchical Vision Transformer for Patient-Level Survival Prediction from Whole Slide Image](https://mlsanity.com/paper/2306.17373/) (2023)
- [Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images](https://mlsanity.com/paper/2109.10778/) (2021)
- [Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction](https://mlsanity.com/paper/2204.09204/) (2022)
- [CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images](https://mlsanity.com/paper/2305.05314/) (2023)
- [Enhancing Label-efficient Medical Image Segmentation with Text-guided Diffusion Models](https://mlsanity.com/paper/2407.05323/) (2024)
- [Pathology-knowledge Enhanced Multi-instance Prompt Learning for Few-shot Whole Slide Image Classification](https://mlsanity.com/paper/2407.10814/) (2024)
- [Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation](https://mlsanity.com/paper/2101.09321/) (2021)
- [Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer](https://mlsanity.com/paper/2301.02933/) (2023)
- [Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image Analysis](https://mlsanity.com/paper/2411.18101/) (2024)
- [Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation](https://mlsanity.com/paper/2211.14491/) (2022)
- [Weakly-Supervised 3D Medical Image Segmentation using Geometric Prior and Contrastive Similarity](https://mlsanity.com/paper/2302.02125/) (2023)
- [CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis](https://mlsanity.com/paper/2106.10230/) (2021)
- [How Good is my Histopathology Vision-Language Foundation Model? A Holistic Benchmark](https://mlsanity.com/paper/2503.12990/) (2025)

## Abstract (arXiv)

Histopathology image analysis plays a critical role in cancer diagnosis and treatment. To automatically segment the cancerous regions, fully supervised segmentation algorithms require labor-intensive and time-consuming labeling at the pixel level. In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image-level labels. Using multiple instance learning (MIL)-based label enrichment, CAMEL splits the image into latticed instances and automatically generates instance-level labels. After label enrichment, the instance-level labels are further assigned to the corresponding pixels, producing the approximate pixel-level labels and making fully supervised training of segmentation models possible. CAMEL achieves comparable performance with the fully supervised approaches in both instance-level classification and pixel-level segmentation on CAMELYON16 and a colorectal adenoma dataset. Moreover, the generality of the automatic labeling methodology may benefit future weakly supervised learning studies for histopathology image analysis.