Mitosis Detection Under Limited Annotation: A Joint Learning Approach
This addresses the challenge of reducing annotation burden for pathologists in cancer diagnosis, though it appears incremental as it builds on existing deep learning approaches.
The paper tackles the problem of mitosis detection in breast cancer histopathology images when limited labeled training data is available, proposing a joint learning framework that improves detection performance with small training sets and achieves results comparable to or better than state-of-the-art methods using full datasets.
Mitotic counting is a vital prognostic marker of tumor proliferation in breast cancer. Deep learning-based mitotic detection is on par with pathologists, but it requires large labeled data for training. We propose a deep classification framework for enhancing mitosis detection by leveraging class label information, via softmax loss, and spatial distribution information among samples, via distance metric learning. We also investigate strategies towards steadily providing informative samples to boost the learning. The efficacy of the proposed framework is established through evaluation on ICPR 2012 and AMIDA 2013 mitotic data. Our framework significantly improves the detection with small training data and achieves on par or superior performance compared to state-of-the-art methods for using the entire training data.