Multi-Organ Cancer Classification and Survival Analysis

Understand

Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology.

• However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between datasets and organ sites.
• In this paper we implement and evaluate a variety of deep neural network models and model ensembles for nuclei classification in renal cell cancer (RCC) and prostate cancer (PCa).
• We propose a convolutional neural network system based on residual learning which significantly improves over the state-of-the-art in cell nuclei classification.