Multi-Organ Cancer Classification and Survival Analysis
This work addresses the need for robust and transferable classification systems in computational pathology to reduce reliance on extensive expert labeling for each cancer type.
The paper tackled the problem of accurate cell nuclei classification in digital pathology by implementing deep neural network models and ensembles for renal and prostate cancer, achieving significant improvements over state-of-the-art methods and showing that combining tissue types during training boosts both classification accuracy and survival analysis.
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. Finally, we show that the combination of tissue types during training increases not only classification accuracy but also overall survival analysis.