# Hierarchical Classification for Improved Histopathology Image Analysis

arXiv: https://arxiv.org/abs/2603.00504 · HTML: https://mlsanity.com/paper/2603.00504/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 28 Feb 2026.

## Builds on (most cited first, 7 of 7)

- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Structured State Space Models for Multiple Instance Learning in Digital Pathology](https://mlsanity.com/paper/2306.15789/) (2023)
- [HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification](https://mlsanity.com/paper/2411.07660/) (2024)
- [Towards a text-based quantitative and explainable histopathology image analysis](https://mlsanity.com/paper/2407.07360/) (2024)
- [CAMP: Continuous and Adaptive Learning Model in Pathology](https://mlsanity.com/paper/2407.09030/) (2024)
- [MECFormer: Multi-task Whole Slide Image Classification with Expert Consultation Network](https://mlsanity.com/paper/2410.04507/) (2024)

## Built on by (most cited first, 1 of 1)

- [TaxoMIL: Taxonomy-Constrained Learning for Hierarchical Whole Slide Image Analysis](https://mlsanity.com/paper/2606.31100/) (2026)

## Abstract (arXiv)

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.