Transformer-based multiple instance learning network with 2D positional encoding for histopathology image classification
Loading...
Date
2025-05
Open Access Location
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Nature Switzerland AG
Rights
(c) 2025 The Author/s
CC BY-NC-ND 4.0
CC BY-NC-ND 4.0
Abstract
Digital medical imaging, particularly pathology images, is essential for cancer diagnosis but faces challenges in direct model training due to its super-resolution nature. Although weakly supervised learning has reduced the need for manual annotations, many multiple instance learning (MIL) methods struggle to effectively capture crucial spatial relationships in histopathological images. Existing methods incorporating positional information often overlook nuanced spatial correlations or use positional encoding strategies that do not fully capture the unique spatial dynamics of pathology images. To address this issue, we propose a new framework named TMIL (Transformer-based Multiple Instance Learning Network with 2D positional encoding), which leverages multiple instance learning for weakly supervised classification of histopathological images. TMIL incorporates a 2D positional encoding module, based on the Transformer, to model positional information and explore correlations between instances. Furthermore, TMIL divides histopathological images into pseudo-bags and trains patch-level feature vectors with deep metric learning to enhance classification performance. Finally, the proposed approach is evaluated on a public colorectal adenoma dataset. The experimental results show that TMIL outperforms existing MIL methods, achieving an AUC of 97.28% and an ACC of 95.19%. These findings suggest that TMIL’s integration of deep metric learning and positional encoding offers a promising approach for improving the efficiency and accuracy of pathology image analysis in cancer diagnosis.
Description
Keywords
Weakly supervised training, Image classification, Multiple instance learning
Citation
Yang B, Ding L, Li J, Li Y, Qu G, Wang J, Wang Q, Liu B. (2025). Transformer-based multiple instance learning network with 2D positional encoding for histopathology image classification. Complex and Intelligent Systems. 11. 5.