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.

本文提出了一种基于注意力机制的多实例学习模型，用于幻灯片级别癌症分级和弱监督区域兴趣检测，并在前列腺癌中进行了实验，其利用可视化显著性图来选择信息瓷砖，提高了细粒度等级分类的性能。