Improving Prostate Cancer Detection with Breast Histopathology Images
This work addresses a domain-specific challenge in medical imaging for prostate cancer diagnosis, but it is incremental as it builds on existing transfer learning methods.
The paper tackled the problem of limited annotated prostate histopathology data by proposing a transfer learning scheme from breast histopathology images, resulting in improved prostate cancer detection performance that outperforms using ImageNet for pre-training.
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.