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Li, Jiayun; Li, Wenyuan; Sisk, Anthony; Ye, Huihui; Wallace, W. Dean; Speier, William; Arnold, Corey W.
Computers in biology and medicine, 04/2021, Letnik: 131Journal Article
Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra-observer agreement. Most previous work on whole slide image analysis has focused on classification or segmentation of small pre-selected regions-of-interest, which requires fine-grained annotation and is non-trivial to extend for large-scale whole slide analysis. In this paper, we proposed a multi-resolution multiple instance learning model that leverages saliency maps to detect suspicious regions for fine-grained grade prediction. Instead of relying on expensive region- or pixel-level annotations, our model can be trained end-to-end with only slide-level labels. The model is developed on a large-scale prostate biopsy dataset containing 20,229 slides from 830 patients. The model achieved 92.7% accuracy, 81.8% Cohen's Kappa for benign, low grade (i.e. Grade group 1) and high grade (i.e. Grade group ≥ 2) prediction, an area under the receiver operating characteristic curve (AUROC) of 98.2% and an average precision (AP) of 97.4% for differentiating malignant and benign slides. The model obtained an AUROC of 99.4% and an AP of 99.8% for cancer detection on an external dataset. Display omitted •A multi-resolution multiple instance learning model is developed for Gleason grade group classification.•The model can localize suspicious regions, and then classify cancer grade at a higher magnification with selected tiles.•The model doesn’t require fine-grained annotations and can be trained with slide-level labels from pathology reports.•The model was evaluated on a large independent test set and an external dataset, and achieved promising results.
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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