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Hierarchical Classification for Improved Histopathology Image Analysis

Keunho Byeon, Jinsol Song, Seong Min Hong, Yosep Chong, Jin Tae Kwak

TL;DR

The proposed HiClass, a hierarchical classification framework for improved histopathology image analysis, that enhances both coarse-grained and fine-grained WSI classification by introducing bidirectional feature integration that facilitates information exchange between coarse-grained and fine-grained feature representations, effectively learning hierarchical features.

Abstract

Whole-slide image analysis is essential for diagnostic tasks in pathology, yet existing deep learning methods primarily rely on flat classification, ignoring hierarchical relationships among class labels. In this study, we propose HiClass, a hierarchical classification framework for improved histopathology image analysis, that enhances both coarse-grained and fine-grained WSI classification. Built based upon a multiple instance learning approach, HiClass extends it by introducing bidirectional feature integration that facilitates information exchange between coarse-grained and fine-grained feature representations, effectively learning hierarchical features. Moreover, we introduce tailored loss functions, including hierarchical consistency loss, intra- and inter-class distance loss, and group-wise cross-entropy loss, to further optimize hierarchical learning. We assess the performance of HiClass on a gastric biopsy dataset with 4 coarse-grained and 14 fine-grained classes, achieving superior classification performance for both coarse-grained classification and fine-grained classification. These results demonstrate the effectiveness of HiClass in improving WSI classification by capturing coarse-grained and fine-grained histopathological characteristics.

Hierarchical Classification for Improved Histopathology Image Analysis

TL;DR

The proposed HiClass, a hierarchical classification framework for improved histopathology image analysis, that enhances both coarse-grained and fine-grained WSI classification by introducing bidirectional feature integration that facilitates information exchange between coarse-grained and fine-grained feature representations, effectively learning hierarchical features.

Abstract

Whole-slide image analysis is essential for diagnostic tasks in pathology, yet existing deep learning methods primarily rely on flat classification, ignoring hierarchical relationships among class labels. In this study, we propose HiClass, a hierarchical classification framework for improved histopathology image analysis, that enhances both coarse-grained and fine-grained WSI classification. Built based upon a multiple instance learning approach, HiClass extends it by introducing bidirectional feature integration that facilitates information exchange between coarse-grained and fine-grained feature representations, effectively learning hierarchical features. Moreover, we introduce tailored loss functions, including hierarchical consistency loss, intra- and inter-class distance loss, and group-wise cross-entropy loss, to further optimize hierarchical learning. We assess the performance of HiClass on a gastric biopsy dataset with 4 coarse-grained and 14 fine-grained classes, achieving superior classification performance for both coarse-grained classification and fine-grained classification. These results demonstrate the effectiveness of HiClass in improving WSI classification by capturing coarse-grained and fine-grained histopathological characteristics.
Paper Structure (18 sections, 3 equations, 2 figures, 3 tables)

This paper contains 18 sections, 3 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Dataset
  4. Model Architecture
  5. Patch-level Encoder
  6. Feature Aggregator
  7. Hierarchical Classifier
  8. Loss Functions
  9. Cross-Entropy Loss
  10. Hierarchical consistency Loss
  11. Intra- and Inter-class Distance Loss
  12. Group-wise Cross-Entropy Loss
  13. Implementation Details
  14. Results
  15. Hierarchical Classification Results
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Overview of HiClass. $N_p$, $N_c$, and $N_f$ represent the number of patches, coarse-grained classes, and fine-grained classes, respectively.
  • Figure 2: Illustration of proposed hierarchy-aware loss functions. (a) Standard cross-entropy loss is applied independently to coarse- and fine-level predictions. (b) Hierarchical consistency loss ($L_{Con}$) aligns the most confident coarse- and fine-level features using Jensen-Shannon divergence. (c) Intra- and inter-class distance loss ($L_{Int}$) encourages coarse-level grouping in the fine-grained feature space through margin-based KL divergence. (d) Group-wise cross-entropy loss ($L_{GCE}$) limits the fine-level prediction space to classes within the predicted coarse category, improving intra-group discrimination.