Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Image

Tingting Zheng, Hongxun Yao, Kui Jiang, Sicheng Zhao, Yi Xiao

TL;DR

A concise yet effective framework, AINet, is developed, which employs a simple predictor and surpasses state-of-the-art methods with substantially fewer FLOPs and parameters.

Abstract

Recent advances in multi-instance learning (MIL) have witnessed impressive performance in whole slide image (WSI) analysis. However, the inherent sparsity of tumors and their morphological diversity lead to obvious heterogeneity across regions, posing significant challenges in aggregating high-quality and discriminative representations. To address this, we introduce a novel concept of anchor instance (AI), a compact subset of instances that are representative within their regions (local) and discriminative at the bag (global) level. These AIs act as semantic references to guide interactions across regions, correcting non-discriminative patterns while preserving regional diversity. Specifically, we propose a dual-level anchor mining (DAM) module to \textbf{select} AIs from massive instances, where the most informative AI in each region is extracted by assessing its similarity to both local and global embeddings. Furthermore, to ensure completeness and diversity, we devise an anchor-guided region correction (ARC) module that explores the complementary information from all regions to \textbf{correct} each regional representation. Building upon DAM and ARC, we develop a concise yet effective framework, AINet, which employs a simple predictor and surpasses state-of-the-art methods with substantially fewer FLOPs and parameters. Moreover, both DAM and ARC are modular and can be seamlessly integrated into existing MIL frameworks, consistently improving their performance.

AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Image

TL;DR

A concise yet effective framework, AINet, is developed, which employs a simple predictor and surpasses state-of-the-art methods with substantially fewer FLOPs and parameters.

Abstract

Recent advances in multi-instance learning (MIL) have witnessed impressive performance in whole slide image (WSI) analysis. However, the inherent sparsity of tumors and their morphological diversity lead to obvious heterogeneity across regions, posing significant challenges in aggregating high-quality and discriminative representations. To address this, we introduce a novel concept of anchor instance (AI), a compact subset of instances that are representative within their regions (local) and discriminative at the bag (global) level. These AIs act as semantic references to guide interactions across regions, correcting non-discriminative patterns while preserving regional diversity. Specifically, we propose a dual-level anchor mining (DAM) module to \textbf{select} AIs from massive instances, where the most informative AI in each region is extracted by assessing its similarity to both local and global embeddings. Furthermore, to ensure completeness and diversity, we devise an anchor-guided region correction (ARC) module that explores the complementary information from all regions to \textbf{correct} each regional representation. Building upon DAM and ARC, we develop a concise yet effective framework, AINet, which employs a simple predictor and surpasses state-of-the-art methods with substantially fewer FLOPs and parameters. Moreover, both DAM and ARC are modular and can be seamlessly integrated into existing MIL frameworks, consistently improving their performance.
Paper Structure (17 sections, 8 equations, 4 figures, 5 tables)

This paper contains 17 sections, 8 equations, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Multi-instance Learning
  4. Instance Selection in WSI classification
  5. Method
  6. MIL Formulation
  7. Overview of AINet
  8. Dual-level Anchor Mining
  9. Anchor-guided Region Correction
  10. Model Optimization
  11. Experiments
  12. Datasets and Implementation Details
  13. Baseline and Evaluation Metrics
  14. Comparison with State-of-the-Art Methods
  15. AINet Generalizablity
  16. ...and 2 more sections

Figures (4)

  • Figure 1: Comparison results of FLOPs (G), parameters (M), and average accuracy with representative MIL methods on TCGA-ESCA, TCGA-BRCA, and BRACS datasets. AINet delivers superior performance with improved efficiency.
  • Figure 2: (a) Regional heterogeneity within a WSI originates from tumor sparsity and morphological diversity. (b) Previous MIL methods often neglect this heterogeneity, resulting in suboptimal feature aggregation. (c) The proposed AINet alleviates this limitation via DAM and ARC modules, yielding more discriminative representations and improved predictive accuracy.
  • Figure 3: Overview of the proposed AINet for WSI classification. Each bag is first divided into multiple spatial regions. The dual-level anchor mining (DAM) module identifies a compact set of anchor instances (AIs), which are then incorporated by the anchor-guided region correction (ARC) module into regional representations to guide the aggregation of heterogeneous features.
  • Figure 4: Hyperparameter effects on the TCGA-ESCA dataset.