A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
This provides an automated solution for pathologists to assess breast cancer proliferation, though it is incremental as it builds on existing deep learning methods for histopathology.
The authors tackled the problem of predicting tumor proliferation scores from breast histopathology images, achieving a quadratic weighted Cohen's kappa of 0.567 for mitosis counting-based scores and a Spearman's correlation coefficient of 0.6171 for molecular data-based scores.
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.