Cancer Metastasis Detection With Neural Conditional Random Field

Abstract

Breast cancer diagnosis often requires accurate detection of metastasis in lymph nodes through Whole-slide Images (WSIs). Recent advances in deep convolutional neural networks (CNNs) have shown significant successes in medical image analysis and particularly in computational histopathology. Because of the outrageous large size of WSIs, most of the methods divide one slide into lots of small image patches and perform classification on each patch independently. However, neighboring patches often share spatial correlations, and ignoring these spatial correlations may result in inconsistent predictions. In this paper, we propose a neural conditional random field (NCRF) deep learning framework to detect cancer metastasis in WSIs. NCRF considers the spatial correlations between neighboring patches through a fully connected CRF which is directly incorporated on top of a CNN feature extractor. The whole deep network can be trained end-to-end with standard back-propagation algorithm with minor computational overhead from the CRF component. The CNN feature extractor can also benefit from considering spatial correlations via the CRF component. Compared to the baseline method without considering spatial correlations, we show that the proposed NCRF framework obtains probability maps of patch predictions with better visual quality. We also demonstrate that our method outperforms the baseline in cancer metastasis detection on the Camelyon16 dataset and achieves an average FROC score of 0.8096 on the test set. NCRF is open sourced at https://github.com/baidu-research/NCRF.

arXiv comment9 pages, 5 figures, MIDL 2018

References in corpus (5)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
• Deep Learning for Identifying Metastatic Breast Cancer
• Detecting Cancer Metastases on Gigapixel Pathology Images
• Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification

Cited by in corpus (8)

• Deep neural network models for computational histopathology: A survey
• Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks
• Deep Learning for Whole Slide Image Analysis: An Overview
• Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey
• CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation
• Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images
• PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis
• Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet