AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Image
#AINet #Anchor instances #Whole slide image #Tumor heterogeneity #Multi-instance learning #Medical AI #Image processing #Computational efficiency
📌 Key Takeaways
- Researchers developed AINet to address tumor heterogeneity challenges in whole slide image analysis
- The approach uses 'anchor instances' as semantic references to guide interactions across regions
- AINet consists of dual-level anchor mining and anchor-guided region correction modules
- The framework achieves superior performance with fewer computational resources than existing methods
- The modular design allows integration with existing MIL frameworks
📖 Full Retelling
Researchers Tingting Zheng, Hongxun Yao, Kui Jiang, Sicheng Zhao, and Yi Xiao introduced a novel approach called AINet (Anchor Instances Learning) for whole slide image analysis on February 21, 2026, addressing the significant challenge of tumor heterogeneity in medical imaging. The paper, submitted to arXiv under the category of Image and Video Processing, presents a solution to the problem of aggregating high-quality representations from whole slide images where tumors exhibit inherent sparsity and morphological diversity across different regions. The researchers identified that conventional multi-instance learning (MIL) methods struggle with the regional heterogeneity found in tumor samples, where variations in morphology and distribution create difficulties in creating discriminative representations. The AINet framework consists of two key components: a dual-level anchor mining module (DAM) and an anchor-guided region correction module (ARC). The DAM extracts the most informative anchor instance in each region by assessing its similarity to both local and global embeddings, while the ARC explores complementary information from all regions to enhance each regional representation. Despite its sophisticated approach, AINet employs a simple predictor and achieves superior performance compared to state-of-the-art methods while using substantially fewer computational resources. Notably, both DAM and ARC are designed as modular components that can be seamlessly integrated into existing MIL frameworks, consistently improving their performance without requiring complete reimplementation.
🏷️ Themes
Medical imaging, Artificial intelligence, Machine learning optimization
📚 Related People & Topics
Tumour heterogeneity
Observation of the diversity of tumour cells
Tumour heterogeneity describes the observation that different tumour cells can show distinct morphological and phenotypic profiles, including cellular morphology, gene expression, metabolism, motility, proliferation, and metastatic potential. This phenomenon occurs both between tumours (inter-tumour...
Entity Intersection Graph
No entity connections available yet for this article.
Original Source
--> Electrical Engineering and Systems Science > Image and Video Processing arXiv:2602.20187 [Submitted on 21 Feb 2026] Title: AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Image Authors: Tingting Zheng , Hongxun Yao , Kui Jiang , Sicheng Zhao , Yi Xiao View a PDF of the paper titled AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Image, by Tingting Zheng and 4 other authors View PDF HTML Abstract: Recent advances in multi-instance learning have witnessed impressive performance in whole slide image 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 , a compact subset of instances that are representative within their regions and discriminative at the bag 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 module to \textbf 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 module that explores the complementary information from all regions to \textbf 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. Subjects: Image and Video Processing (eess.IV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.20187 [eess.IV]...
Read full article at source