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

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.

8 pages, 4 figures, CVPR 2019 Towards Causal, Explainable and Universal Medical Visual Diagnosis (MVD) Workshop

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Cited by in corpus (8)

• Deep neural network models for computational histopathology: A survey
• Augmenting the Pathology Lab: An Intelligent Whole Slide Image Classification System for the Real World
• WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images
• A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers
• Attention-Guided Discriminative Region Localization and Label Distribution Learning for Bone Age Assessment
• Deep Learning for Prostate Pathology
• Kernel Self-Attention in Deep Multiple Instance Learning
• Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline