Detection and Classification of Breast Cancer Metastates Based on U-Net
This work addresses a domain-specific medical imaging problem for breast cancer diagnosis, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled breast cancer metastasis detection and classification in lymph nodes using a U-Net-based pipeline, achieving a Kappa score of 0.902 on training data.
This paper presents U-net based breast cancer metastases detection and classification in lymph nodes, as well as patient-level classification based on metastases detection. The whole pipeline can be divided into five steps: preprocessing and data argumentation, patch-based segmentation, post processing, slide-level classification, and patient-level classification. In order to reduce overfitting and speedup convergence, we applied batch normalization and dropout into U-Net. The final Kappa score reaches 0.902 on training data.