Coupling weak and strong supervision for classification of prostate cancer histopathology images

Abstract

Automated grading of prostate cancer histopathology images is a challenging task, with one key challenge being the scarcity of annotations down to the level of regions of interest (strong labels), as typically the prostate cancer Gleason score is known only for entire tissue slides (weak labels). In this study, we focus on automated Gleason score assignment of prostate cancer whole-slide images on the basis of a large weakly-labeled dataset and a smaller strongly-labeled one. We efficiently leverage information from both label sources by jointly training a classifier on the two datasets and by introducing a gradient update scheme that assigns different relative importances to each training example, as a means of self-controlling the weak supervision signal. Our approach achieves superior performance when compared with standard Gleason scoring methods.

arXiv commentAccepted in Medical Imaging meets NIPS Workshop, NIPS 2018

References in corpus (2)

• Adam: A Method for Stochastic Optimization
• Deep CORAL: Correlation Alignment for Deep Domain Adaptation

Cited by in corpus (5)

• WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images
• Computer-Aided Assessment of Catheters and Tubes on Radiographs: How Good is Artificial Intelligence for Assessment?
• An attention-based multi-resolution model for prostate whole slide imageclassification and localization
• Improving Prostate Cancer Detection with Breast Histopathology Images
• A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning