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Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images

Ravi Kant Gupta, Shounak Das, Amit Sethi

TL;DR

This breakthrough equips 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.

Abstract

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.

Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images

TL;DR

This breakthrough equips 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.

Abstract

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.

Paper Structure

This paper contains 8 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Experimentation and Dataset
  5. Experimentation
  6. Dataset
  7. Results
  8. Conclusions and Discussions

Figures (4)

  • Figure 1: Thumbnail image samples of TCGA Lung data coudray2018classification (first row) and Camelyon17 dataset bandi2018detection (bottom row)
  • Figure 2: Preprocessing pipeline: (a) Snapshot of sample image, (b) Tissue detection, (c) Tiling of WSI, and (d) Patches
  • Figure 3: Elbow plot illustrating the variation of within-cluster sum of squares (WCSS) for k = 2 to 30 in K-means clustering using the (a) TCGA Dataset and (b) Camelyon17 Dataset
  • Figure 4: Model Workflow