Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images
This addresses the problem of limited practical utility of WSIs due to their size for medical diagnostics and research, though it appears incremental as it builds on existing encoders and classification models.
The paper tackles the computational challenges of analyzing gigapixel-sized Whole Slide Images (WSIs) in digital pathology by proposing a method that distills WSI information into a single vector for permutation-invariant classification, enhancing computational efficiency and enabling more accurate pathological assessments.
Whole Slide Imaging (WSI) is a cornerstone of digital pathology, offering detailed insights critical for diagnosis and research. Yet, the gigapixel size of WSIs imposes significant computational challenges, limiting their practical utility. Our novel approach addresses these challenges by leveraging various encoders for intelligent data reduction and employing a different classification model to ensure robust, permutation-invariant representations of WSIs. A key innovation of our method is the ability to distill the complex information of an entire WSI into a single vector, effectively capturing the essential features needed for accurate analysis. This approach significantly enhances the computational efficiency of WSI analysis, enabling more accurate pathological assessments without the need for extensive computational resources. This breakthrough equips us with the capability to effectively address the challenges posed by large image resolutions in whole-slide imaging, paving the way for more scalable and effective utilization of WSIs in medical diagnostics and research, marking a significant advancement in the field.