Automatic breast cancer grading in lymph nodes using a deep neural network
This work addresses breast cancer diagnosis for medical professionals, but it is incremental as it applies existing deep learning techniques to a specific medical imaging task.
The authors tackled breast cancer grading in lymph node whole-slide images by developing a deep neural network method that classifies patches and aggregates results to predict patient-level grades, achieving fast processing speeds for high-throughput analysis.
The progression of breast cancer can be quantified in lymph node whole-slide images (WSIs). We describe a novel method for effectively performing classification of whole-slide images and patient level breast cancer grading. Our method utilises a deep neural network. The method performs classification on small patches and uses model averaging for boosting. In the first step, region of interest patches are determined and cropped automatically by color thresholding and then classified by the deep neural network. The classification results are used to determine a slide level class and for further aggregation to predict a patient level grade. Fast processing speed of our method enables high throughput image analysis.