# A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology

arXiv: https://arxiv.org/abs/1612.07180 · HTML: https://mlsanity.com/paper/1612.07180/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2016.

## Built on by (most cited first, 3 of 3)

- [Challenging mitosis detection algorithms: Global labels allow centroid localization](https://mlsanity.com/paper/2211.16852/) (2022)
- [BOrg: A Brain Organoid-Based Mitosis Dataset for Automatic Analysis of Brain Diseases](https://mlsanity.com/paper/2406.19556/) (2024)
- [Designing an Improved Deep Learning-based Model for COVID-19 Recognition in Chest X-ray Images: A Knowledge Distillation Approach](https://mlsanity.com/paper/2301.02735/) (2023)

## Abstract (arXiv)

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.