Feedback-based Self-improving CNN Algorithm for Breast Cancer Lymph Node Metastasis Detection in Real Clinical Environment
Maryam Sadeghi, Iván Maldonado, Niklas Abele, Johannes Haybaeck, Axel Boese, Prabal Poudel, Michael Friebe · 2019
Digital pathology can be thought of as a model composed of 3 main elements; classification algorithm, Graphical User Interface (GUI) and the pathologists. Currently there is only a one way interaction from the classification algorithm to the pathologist. This paper, proposes an additional backward path which is a new feedback-based method, aimed to improve the performance of the classification algorithms by utilizing the feedback of the pathologists. The GUI developed for this purpose, is aimed to be simple and adaptive to different classification algorithms. The method showed significant improvement in the classification performance of the applied Convolutional Neural Network (CNN) algorithm. The 25% quantile of the probability score of the predictions increased from 0.48 to 0.89 and the median of the data increased from 0.95 to 0.99.