Predicting tumor proliferation scores is an important biomarker indicative of breast cancer patients' prognosis. In this paper, we present a unified framework to predict tumor proliferation scores from whole slide images in breast histopathology. The proposed system is 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 prediction on the molecular data based score, the system obtained 0.6171. Our system won first place in all of the three tasks in Tumor Proliferation Assessment Challenge at MICCAI 2016, outperforming all other approaches.
Kyunghyun Paeng1, Sangheum Hwang1, Sunggyun Park1, Minsoo Kim1 and Seokhwi Kim2
1Lunit Inc., 2KAIST