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

TL;DR

This paper introduces an attention-based multi-resolution model for prostate cancer grading that improves slide-level classification accuracy and enables weakly-supervised localization of regions of interest, using large-scale histology data.

Contribution

It presents a novel two-stage attention-based multiple instance learning approach that enhances prostate cancer grading and ROI detection with weak supervision.

Findings

Achieved 85.11% accuracy in prostate cancer grading.

Utilized visual saliency maps for informative tile selection.

Demonstrated state-of-the-art performance on large-scale dataset.

Abstract

Histology review is often used as the `gold standard' for disease diagnosis. Computer aided diagnosis tools can potentially help improve current pathology workflows by reducing examination time and interobserver variability. Previous work in cancer grading has focused mainly on classifying pre-defined regions of interest (ROIs), or relied on large amounts of fine-grained labels. In this paper, we propose a two-stage attention-based multiple instance learning model for slide-level cancer grading and weakly-supervised ROI detection and demonstrate its use in prostate cancer. Compared with existing Gleason classification models, our model goes a step further by utilizing visualized saliency maps to select informative tiles for fine-grained grade classification. The model was primarily developed on a large-scale whole slide dataset consisting of 3,521 prostate biopsy slides with only slide-level labels from 718 patients. The model achieved state-of-the-art performance for prostate cancer 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) slides on an independent test set.