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Histology review is often used as the `gold standard' for disease diagnosis.Computer aided diagnosis tools can potentially help improve current pathologyworkflows by reducing examination time and interobserver variability. Previouswork in cancer grading has focused mainly on classifying pre-defined regions ofinterest (ROIs), or relied on large amounts of fine-grained labels. In thispaper, we propose a two-stage attention-based multiple instance learning modelfor slide-level cancer grading and weakly-supervised ROI detection anddemonstrate its use in prostate cancer. Compared with existing Gleasonclassification models, our model goes a step further by utilizing visualizedsaliency maps to select informative tiles for fine-grained gradeclassification. The model was primarily developed on a large-scale whole slidedataset consisting of 3,521 prostate biopsy slides with only slide-level labelsfrom 718 patients. The model achieved state-of-the-art performance for prostatecancer grading with an accuracy of 85.11\% for classifying benign, low-grade(Gleason grade 3+3 or 3+4), and high-grade (Gleason grade 4+3 or higher) slideson an independent test set.

An attention-based multi-resolution model for prostate whole slide imageclassification and localization