Abstract:
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.
