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LCA-Med: A lightweight cross-modal adaptive feature processing module for detecting imbalanced medical image

Xiang Li¹, Long Lan², Husam Lahza³

¹College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China; Department of Intelligent Data Science, College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China; Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Chinese Medicine Guangdong Laboratory, Hengqin 519031, Guangdong, China.

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Summary

This study introduces LCA-Med, a lightweight module for cross-modal medical image detection, improving accuracy despite data distribution discrepancies. The LCA-Med CNX method demonstrates superior performance on imbalanced datasets, advancing medical image analysis.

Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Data distribution discrepancy across medical image datasets hinders cross-domain adaptive detection accuracy.
Existing methods struggle with heterogeneity in medical imaging modalities and data imbalance.

Purpose of the Study:

To propose a novel lightweight cross-modal adaptive detection module (LCA-Med) for medical images.
To develop an advanced cross-modal medical image adaptive detection method (LCA-Med CNX) and a new training paradigm.
To enhance the accuracy and robustness of medical image detection, especially in imbalanced data scenarios.

Main Methods:

Developed LCA-Med, a lightweight feature preprocessor extracting pathological information from varied image modalities guided by text prompts.

Keywords:

Adaptive detection Cross-domain Cross-modal Lightweight Medical Image Prompt

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Introduced LCA-Med CNX, a cross-modal medical image adaptive detection method.

Proposed a novel training paradigm using generated dataset groups, an attention module, and a meta-heuristic algorithm.

Main Results:

LCA-Med CNX achieved state-of-the-art performance on five out of six medical image datasets.
The proposed method demonstrated superior performance compared to ten existing methods, particularly on imbalanced datasets.
The lightweight LCA-Med module showed effective integration and feature extraction capabilities.

Conclusions:

The proposed LCA-Med CNX method and training paradigm significantly improve cross-domain adaptive detection in medical imaging.
The approach is effective in addressing data distribution discrepancies and data imbalance challenges.
LCA-Med offers a versatile and efficient solution for feature extraction in multimodal medical image analysis.