Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding

Abstract

Convolutional neural networks can be trained to perform histology slide classification using weak annotations with multiple instance learning (MIL). However, given the paucity of labeled histology data, direct application of MIL can easily suffer from overfitting and the network is unable to learn rich feature representations due to the weak supervisory signal. We propose to overcome such limitations with a two-stage semi-supervised approach that combines the power of data-efficient self-supervised feature learning via contrastive predictive coding (CPC) and the interpretability and flexibility of regularized attention-based MIL. We apply our two-stage CPC + MIL semi-supervised pipeline to the binary classification of breast cancer histology images. Across five random splits, we report state-of-the-art performance with a mean validation accuracy of 95% and an area under the ROC curve of 0.968. We further evaluate the quality of features learned via CPC relative to simple transfer learning and show that strong classification performance using CPC features can be efficiently leveraged under the MIL framework even with the feature encoder frozen.

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• Conditional Image Generation with PixelCNN Decoders
• Pixel Recurrent Neural Networks
• Data-Efficient Image Recognition with Contrastive Predictive Coding
• BACH: Grand Challenge on Breast Cancer Histology Images
• Detecting Cancer Metastases on Gigapixel Pathology Images
• Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues
• Cluster-Based Learning from Weakly Labeled Bags in Digital Pathology

Cited by in corpus (7)

• Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification
• Self-Supervision Closes the Gap Between Weak and Strong Supervision in Histology
• Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning
• Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks
• Kernel Self-Attention in Deep Multiple Instance Learning
• Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation
• Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning