# Improving Prostate Cancer Detection with Breast Histopathology Images

arXiv: https://arxiv.org/abs/1903.05769 · HTML: https://mlsanity.com/paper/1903.05769/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Improving Feature Extraction from Histopathological Images Through A Fine-tuning ImageNet Model](https://mlsanity.com/paper/2201.00636/) (2022)
- [Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection](https://mlsanity.com/paper/2109.09518/) (2021)
- [Embracing Annotation Efficient Learning (AEL) for Digital Pathology and Natural Images](https://mlsanity.com/paper/2212.00470/) (2022)
- [Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study](https://mlsanity.com/paper/2309.17223/) (2023)

## Abstract (arXiv)

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.