2016/12/21 by Kyunghyun Paeng, Sangheum Hwang, Paeng, Kyunghyun +5
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #cs.CV
paper · pdf · doi:10.48550/arxiv.1612.07180
Accepted to the 3rd Workshop on Deep Learning in Medical Image Analysis (DLMIA 2017), MICCAI 2017
arxiv created 2017/08/11 · arxiv updated 2017/08/14
We present a unified framework to predict tumor proliferation scores from breast histopathology whole slide images. Our system offers a fully automated solution to predicting both a molecular data-based, and a mitosis counting-based tumor proliferation score. The framework integrates three modules, each fine-tuned to maximize the overall performance: An image processing component for handling whole slide images, a deep learning based mitosis detection network, and a proliferation scores prediction module. We have achieved 0.567 quadratic weighted Cohen's kappa in mitosis counting-based score prediction and 0.652 F1-score in mitosis detection. On Spearman's correlation coefficient, which evaluates predictive accuracy on the molecular data based score, the system obtained 0.6171. Our approach won first place in all of the three tasks in Tumor Proliferation Assessment Challenge 2016 which is MICCAI grand challenge.