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.

我们提出了一个统一的框架，从乳腺组织病理学全幻灯片图像中预测肿瘤增殖评分。该系统提供了完全自动化的解决方案，预测基于分子数据和有丝分裂计数的肿瘤增殖评分。我们的方法在 Tumor Proliferation Assessment Challenge 2016 中获得了三个任务的第一名。