# Enhancing Clinically Significant Prostate Cancer Prediction in T2-weighted Images through Transfer Learning from Breast Cancer

arXiv: https://arxiv.org/abs/2405.07869 · HTML: https://mlsanity.com/paper/2405.07869/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 13 May 2024.

## Builds on (most cited first, 3 of 3)

- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [Prostate Cancer Detection using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1905.13145/) (2019)
- [Enhancing Clinical Support for Breast Cancer with Deep Learning Models using Synthetic Correlated Diffusion Imaging](https://mlsanity.com/paper/2211.05308/) (2022)

## Built on by (most cited first, 1 of 1)

- [Enhancing Trust in Clinically Significant Prostate Cancer Prediction with Multiple Magnetic Resonance Imaging Modalities](https://mlsanity.com/paper/2411.04662/) (2024)

## Abstract (arXiv)

In 2020, prostate cancer saw a staggering 1.4 million new cases, resulting in over 375,000 deaths. The accurate identification of clinically significant prostate cancer is crucial for delivering effective treatment to patients. Consequently, there has been a surge in research exploring the application of deep neural networks to predict clinical significance based on magnetic resonance images. However, these networks demand extensive datasets to attain optimal performance. Recently, transfer learning emerged as a technique that leverages acquired features from a domain with richer data to enhance the performance of a domain with limited data. In this paper, we investigate the improvement of clinically significant prostate cancer prediction in T2-weighted images through transfer learning from breast cancer. The results demonstrate a remarkable improvement of over 30% in leave-one-out cross-validation accuracy.