Automated Classification Map Generation of Prostate Cancer using Deep Learning
Wenhan Tan, David E. Breen, Fernando U. Garcia, Mark D. Zarella · 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2021
Whole slide images are examined by pathologists and scored according to the Gleason grading system. It is a time-consuming task and may involve assessing variability between different pathologists. In this work, a deep learning system is presented that generates classification maps for whole slide images. This system produces patch-level results first and then predicts a classification map for each prostate cancer slide. The classification maps contain regional cancer severity for each biopsy and are compared with provided mask images. Both provided mask images and predicted mask images are then reviewed by an experienced pathologist to evaluate classification performance. Most state-of-the-art deep learning methods cannot explain how they output classification results. With this work’s classification maps, pathologists can see the regional classification results that explain the algorithm’s classification.