Trusted Multi-Scale Classification Framework for Whole Slide Image
This work addresses a domain-specific problem for medical imaging and pathology, offering an incremental improvement by integrating uncertainty estimation and multi-scale analysis into WSI classification.
The paper tackles the classification of gigapixel whole-slide images (WSI) by addressing limitations in computing resources, multi-scale knowledge utilization, and uncertainty estimation, proposing a trusted multi-scale framework that significantly improves performance compared to state-of-the-art methods on two benchmark databases.
Despite remarkable efforts been made, the classification of gigapixels whole-slide image (WSI) is severely restrained from either the constrained computing resources for the whole slides, or limited utilizing of the knowledge from different scales. Moreover, most of the previous attempts lacked of the ability of uncertainty estimation. Generally, the pathologists often jointly analyze WSI from the different magnifications. If the pathologists are uncertain by using single magnification, then they will change the magnification repeatedly to discover various features of the tissues. Motivated by the diagnose process of the pathologists, in this paper, we propose a trusted multi-scale classification framework for the WSI. Leveraging the Vision Transformer as the backbone for multi branches, our framework can jointly classification modeling, estimating the uncertainty of each magnification of a microscope and integrate the evidence from different magnification. Moreover, to exploit discriminative patches from WSIs and reduce the requirement for computation resources, we propose a novel patch selection schema using attention rollout and non-maximum suppression. To empirically investigate the effectiveness of our approach, empirical experiments are conducted on our WSI classification tasks, using two benchmark databases. The obtained results suggest that the trusted framework can significantly improve the WSI classification performance compared with the state-of-the-art methods.