Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds

TLDR

The rapid growth of medical imaging, driven by improved hardware and deep learning, has increased diagnostic accuracy but also workload, prompting research into attention mechanisms that achieve state‑of‑the‑art results. This review aims to evaluate deep‑learning attention methods for auxiliary diagnosis and to assess their potential to improve diagnostic efficiency. The authors conduct a comprehensive literature survey, analyze key terms, describe the evolution and technical features of attention mechanisms, and summarize their application to classification, segmentation, detection, and enhancement while outlining remaining challenges and future research directions.

Abstract

Survey/review study Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds Xiang Li 1, Minglei Li 1, Pengfei Yan 1, Guanyi Li 1, Yuchen Jiang 1, Hao Luo 1,*, and Shen Yin 2 1 Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China 2 Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim 7034, Norway * Correspondence: [e-mail] Received: 16 October 2022 Accepted: 25 November 2022 Published: 27 March 2023 Abstract: With the improvement of hardware computing power and the development of deep learning algorithms, a revolution of "artificial intelligence (AI) + medical image" is taking place. Benefiting from diversified modern medical measurement equipment, a large number of medical images will be produced in the clinical process. These images improve the diagnostic accuracy of doctors, but also increase the labor burden of doctors. Deep learning technology is expected to realize an auxiliary diagnosis and improve diagnostic efficiency. At present, the method of deep learning technology combined with attention mechanism is a research hotspot and has achieved state-of-the-art results in many medical image tasks. This paper reviews the deep learning attention methods in medical image analysis. A comprehensive literature survey is first conducted to analyze the keywords and literature. Then, we introduce the development and technical characteristics of the attention mechanism. For its application in medical image analysis, we summarize the related methods in medical image classification, segmentation, detection, and enhancement. The remaining challenges, potential solutions, and future research directions are also discussed.