# CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images

arXiv: https://arxiv.org/abs/2305.05314 · HTML: https://mlsanity.com/paper/2305.05314/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 48 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [Nystr"omformer: A Nystr"om-Based Algorithm for Approximating Self-Attention](https://mlsanity.com/paper/2102.03902/) (2021)
- [DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2203.12081/) (2022)
- [Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks](https://mlsanity.com/paper/2009.11169/) (2020)
- [Deep neural network models for computational histopathology: A survey](https://mlsanity.com/paper/1912.12378/) (2019)
- [Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification](https://mlsanity.com/paper/1504.07947/) (2015)
- [Revisiting Multiple Instance Neural Networks](https://mlsanity.com/paper/1610.02501/) (2016)
- [A graph-transformer for whole slide image classification](https://mlsanity.com/paper/2205.09671/) (2022)
- [CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation](https://mlsanity.com/paper/1908.10555/) (2019)
- [Neural Image Compression for Gigapixel Histopathology Image Analysis](https://mlsanity.com/paper/1811.02840/) (2018)
- [Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2103.10626/) (2021)
- [Representation Learning of Histopathology Images using Graph Neural Networks](https://mlsanity.com/paper/2004.07399/) (2020)
- [A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning](https://mlsanity.com/paper/2011.02679/) (2020)
- [Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach](https://mlsanity.com/paper/1802.02212/) (2018)
- [Multiple instance learning with graph neural networks](https://mlsanity.com/paper/1906.04881/) (2019)
- [MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer Diagnosis](https://mlsanity.com/paper/2211.05862/) (2022)

## Built on by (most cited first, 13 of 13)

- [Sm: enhanced localization in Multiple Instance Learning for medical imaging classification](https://mlsanity.com/paper/2410.03276/) (2024)
- [Contrastive Integrated Gradients: A Feature Attribution-Based Method for Explaining Whole Slide Image Classification](https://mlsanity.com/paper/2511.08464/) (2025)
- [PSA-MIL: A Probabilistic Spatial Attention-Based Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2503.16284/) (2025)
- [torchmil: A PyTorch-based library for deep Multiple Instance Learning](https://mlsanity.com/paper/2509.08129/) (2025)
- [BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology](https://mlsanity.com/paper/2503.20880/) (2025)
- [GMAT: Grounded Multi-Agent Clinical Description Generation for Text Encoder in Vision-Language MIL for Whole Slide Image Classification](https://mlsanity.com/paper/2508.01293/) (2025)
- [Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning](https://mlsanity.com/paper/2512.17788/) (2025)
- [Hepato-LLaVA: An Expert MLLM with Sparse Topo-Pack Attention for Hepatocellular Pathology Analysis on Whole Slide Images](https://mlsanity.com/paper/2602.19424/) (2026)
- [Topological Inductive Bias fosters Multiple Instance Learning in Data-Scarce Scenarios](https://mlsanity.com/paper/2307.14025/) (2023)
- [Revisiting Data Challenges of Computational Pathology: A Pack-based Multiple Instance Learning Training Framework](https://mlsanity.com/paper/2509.20923/) (2025)
- [PA-MIL: Phenotype-Aware Multiple Instance Learning Guided by Language Prompting and Genotype-to-Phenotype Relationships](https://mlsanity.com/paper/2602.02558/) (2026)
- [Parameter-efficient Prompt Tuning and Hierarchical Textual Guidance for Few-shot Whole Slide Image Classification](https://mlsanity.com/paper/2603.21504/) (2026)
- [Conveyance: A Versatile Framework for Learning in Structured Class Spaces](https://mlsanity.com/paper/2605.28420/) (2026)

## Abstract (arXiv)

The visual examination of tissue biopsy sections is fundamental for cancer diagnosis, with pathologists analyzing sections at multiple magnifications to discern tumor cells and their subtypes. However, existing attention-based multiple instance learning (MIL) models used for analyzing Whole Slide Images (WSIs) in cancer diagnostics often overlook the contextual information of tumor and neighboring tiles, leading to misclassifications. To address this, we propose the Context-Aware Multiple Instance Learning (CAMIL) architecture. CAMIL incorporates neighbor-constrained attention to consider dependencies among tiles within a WSI and integrates contextual constraints as prior knowledge into the MIL model. We evaluated CAMIL on subtyping non-small cell lung cancer (TCGA-NSCLC) and detecting lymph node (CAMELYON16 and CAMELYON17) metastasis, achieving test AUCs of 97.5%, 95.9%, and 88.1%, respectively, outperforming other state-of-the-art methods. Additionally, CAMIL enhances model interpretability by identifying regions of high diagnostic value.