Triple-kernel Gated Attention-based Multiple Instance Learning with Contrastive Learning for Medical Image Analysis

Coenen, Frans ORCID: 0000-0003-1026-6649, Ye, Ruijie, Hu, Huafeng, Thiyagalingam, Jeyarajan and Su, Jionglong (2023) Triple-kernel Gated Attention-based Multiple Instance Learning with Contrastive Learning for Medical Image Analysis. Applied Intelligence, 53 (17). pp. 1-16.

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Official URL: http://dx.doi.org/10.1007/s10489-023-04458-y

Abstract

In machine learning, multiple instance learning is a method evolved from supervised learning algorithms, which defines a "bag" as a collection of multiple examples with a wide range of applications. In this paper, we propose a novel deep multiple instance learning model for medical image analysis, called triple-kernel gated attention-based multiple instance learning with contrastive learning. It can be used to overcome the limitations of the existing multiple instance learning approaches to medical image analysis. Our model consists of four steps. i) Extracting the representations by a simple convolutional neural network using contrastive learning for training. ii) Using three different kernel functions to obtain the importance of each instance from the entire image and forming an attention map. iii) Based on the attention map, aggregating the entire image together by attention-based MIL pooling. iv) Feeding the results into the classifier for prediction. The results on different datasets demonstrate that the proposed model outperforms state-of-the-art methods on binary and weakly supervised classification tasks. It can provide more efficient classification results for various disease models and additional explanatory information.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Medical image analysis, Multiple instance learning
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	15 Mar 2023 09:42
Last Modified:	04 Apr 2024 01:30
DOI:	10.1007/s10489-023-04458-y
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3169061