  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.
