Cart
Create Account
Sign In
Already purchased this program?
Login to View
This video program is a part of the Premium package:
Weakly Supervised Prostate TMA Classification Via Graph Convolutional Networks
- IEEE MemberUS $11.00
- Society MemberUS $0.00
- IEEE Student MemberUS $11.00
- Non-IEEE MemberUS $15.00
Weakly Supervised Prostate TMA Classification Via Graph Convolutional Networks
Histology-based grade classification is clinically important for many cancer types in stratifying patients into distinct treatment groups. In prostate cancer, the Gleason score is a grading system used to measure the aggressiveness of prostate cancer from the spatial organization of cells and the distribution of glands. However, the subjective interpretation of Gleason score often suffers from large interobserver and intraobserver variability. Previous work in deep learning-based objective Gleason grading requires manual pixel-level annotation. In this work, we propose a weakly-supervised approach for grade classification in tissue micro-arrays (TMA) using graph convolutional networks (GCNs), in which we model the spatial organization of cells as a graph to better capture the proliferation and community structure of tumor cells. We learn the morphometry of each cell using a contrastive predictive coding (CPC)-based self-supervised approach. Using five-fold cross-validation we demonstrate that our method can achieve a 0.9637?0.0131 AUC using only TMA-level labels. Our method also demonstrates a 36.36% improvement in AUC over standard GCNs with texture features and a 15.48% improvement over GCNs with VGG19 features. Our proposed pipeline can be used to objectively stratify low and high-risk cases, reducing inter- and intra-observer variability and pathologist workload.
Histology-based grade classification is clinically important for many cancer types in stratifying patients into distinct treatment groups. In prostate cancer, the Gleason score is a grading system used to measure the aggressiveness of prostate cancer from the spatial organization of cells and the distribution of glands. However, the subjective interpretation of Gleason score often suffers from large interobserver and intraobserver variability. Previous work in deep learning-based objective Gleason grading requires manual pixel-level annotation. In this work, we propose a weakly-supervised approach for grade classification in tissue micro-arrays (TMA) using graph convolutional networks (GCNs), in which we model the spatial organization of cells as a graph to better capture the proliferation and community structure of tumor cells. We learn the morphometry of each cell using a contrastive predictive coding (CPC)-based self-supervised approach. Using five-fold cross-validation we demonstrate that our method can achieve a 0.9637?0.0131 AUC using only TMA-level labels. Our method also demonstrates a 36.36% improvement in AUC over standard GCNs with texture features and a 15.48% improvement over GCNs with VGG19 features. Our proposed pipeline can be used to objectively stratify low and high-risk cases, reducing inter- and intra-observer variability and pathologist workload.
