# Improving Prostate Cancer Detection with Breast Histopathology Images

## TL;DR

This paper introduces a transfer learning approach from breast to prostate histopathology images, significantly improving prostate cancer detection by leveraging a publicly available breast dataset instead of traditional ImageNet pre-training.

## Contribution

The study proposes a novel cross-cancer transfer learning scheme that enhances prostate cancer detection performance over conventional ImageNet-based methods.

## Key findings

- Cross-cancer transfer learning outperforms ImageNet pre-training.
- Using breast histopathology data improves prostate cancer classification.
- The approach benefits from publicly available datasets for better model performance.

## Abstract

Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images. With the help of transfer learning, classification and segmentation performance of neural network models have been further increased. However, due to the absence of large, extensively annotated, publicly available prostate histopathology datasets, several previous studies employ datasets from well-studied computer vision tasks such as ImageNet dataset. In this work, we propose a transfer learning scheme from breast histopathology images to improve prostate cancer detection performance. We validate our approach on annotated prostate whole slide images by using a publicly available breast histopathology dataset as pre-training. We show that the proposed cross-cancer approach outperforms transfer learning from ImageNet dataset.