# Detecting Cancer Metastases on Gigapixel Pathology Images

arXiv: https://arxiv.org/abs/1703.02442 · HTML: https://mlsanity.com/paper/1703.02442/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2017.

## Built on by (most cited first, 30 of 52)

- [Federated Unlearning with Knowledge Distillation](https://mlsanity.com/paper/2201.09441/) (2022)
- [Supervised Transfer Learning at Scale for Medical Imaging](https://mlsanity.com/paper/2101.05913/) (2021)
- [Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training](https://mlsanity.com/paper/2205.04723/) (2022)
- [Subspace based Federated Unlearning](https://mlsanity.com/paper/2302.12448/) (2023)
- [MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification](https://mlsanity.com/paper/2108.10709/) (2021)
- [Resolution-Based Distillation for Efficient Histology Image Classification](https://mlsanity.com/paper/2101.04170/) (2021)
- [On the Origins of the Block Structure Phenomenon in Neural Network Representations](https://mlsanity.com/paper/2202.07184/) (2022)
- [Efficient High-Resolution Deep Learning: A Survey](https://mlsanity.com/paper/2207.13050/) (2022)
- [PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification](https://mlsanity.com/paper/2111.01557/) (2021)
- [Improving Representation Learning for Histopathologic Images with Cluster Constraints](https://mlsanity.com/paper/2310.12334/) (2023)
- [Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization](https://mlsanity.com/paper/2204.09334/) (2022)
- [Evaluating histopathology transfer learning with ChampKit](https://mlsanity.com/paper/2206.06862/) (2022)
- [Large-scale pretraining on pathological images for fine-tuning of small pathological benchmarks](https://mlsanity.com/paper/2303.15693/) (2023)
- [Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning](https://mlsanity.com/paper/2306.00003/) (2023)
- [Rethinking Machine Learning Model Evaluation in Pathology](https://mlsanity.com/paper/2204.05205/) (2022)
- [Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification](https://mlsanity.com/paper/2206.13123/) (2022)
- [A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images](https://mlsanity.com/paper/2303.11019/) (2023)
- [SaccadeDet: A Novel Dual-Stage Architecture for Rapid and Accurate Detection in Gigapixel Images](https://mlsanity.com/paper/2407.17956/) (2024)
- [Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization](https://mlsanity.com/paper/2407.02900/) (2024)
- [Self-supervised learning-based general laboratory progress pretrained model for cardiovascular event detection](https://mlsanity.com/paper/2303.06980/) (2023)
- [SLPerf: a Unified Framework for Benchmarking Split Learning](https://mlsanity.com/paper/2304.01502/) (2023)
- [Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology](https://mlsanity.com/paper/2304.08498/) (2023)
- [Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation](https://mlsanity.com/paper/2307.07168/) (2023)
- [Conformal Object Detection by Sequential Risk Control](https://mlsanity.com/paper/2505.24038/) (2025)
- [An Efficient Cervical Whole Slide Image Analysis Framework Based on Multi-scale Semantic and Location Deep Features](https://mlsanity.com/paper/2106.15113/) (2021)
- [Increasing a microscope's effective field of view via overlapped imaging and machine learning](https://mlsanity.com/paper/2110.04921/) (2021)
- [Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification](https://mlsanity.com/paper/2204.01728/) (2022)
- [HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learning](https://mlsanity.com/paper/2209.06185/) (2022)
- [Patch Gradient Descent: Training Neural Networks on Very Large Images](https://mlsanity.com/paper/2301.13817/) (2023)
- [An XAI Approach to Deep Learning Models in the Detection of DCIS](https://mlsanity.com/paper/2106.14186/) (2021)

## Abstract (arXiv)

Each year, the treatment decisions for more than 230,000 breast cancer patients in the U.S. hinge on whether the cancer has metastasized away from the breast. Metastasis detection is currently performed by pathologists reviewing large expanses of biological tissues. This process is labor intensive and error-prone. We present a framework to automatically detect and localize tumors as small as 100 x 100 pixels in gigapixel microscopy images sized 100,000 x 100,000 pixels. Our method leverages a convolutional neural network (CNN) architecture and obtains state-of-the-art results on the Camelyon16 dataset in the challenging lesion-level tumor detection task. At 8 false positives per image, we detect 92.4% of the tumors, relative to 82.7% by the previous best automated approach. For comparison, a human pathologist attempting exhaustive search achieved 73.2% sensitivity. We achieve image-level AUC scores above 97% on both the Camelyon16 test set and an independent set of 110 slides. In addition, we discover that two slides in the Camelyon16 training set were erroneously labeled normal. Our approach could considerably reduce false negative rates in metastasis detection.