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

Data distribution discrepancy across datasets is one of the major obstacles hindering the improvement of the accuracy of cross-domain adaptive detection of medical images. To address this challenge, we propose a novel lightweight cross-modal adaptive detection module named LCA-Med (LCaM). The proposed module boasts a lightweight structure and a minimalistic parameter count, thereby facilitating its integration into the anterior segment of a diverse array of foundational and downstream networks. It is adept at serving as a feature preprocessor, proficiently extracting pertinent information regrading pathologies from a array of images (image modality) produced through varied medical imaging techniques, all guided by the input of prompts (text modality). We also propose a novel cross-modal medical image adaptive detection method, LCA-Med CNX (LCaM-CNX), and a novel cross-domain adaptive detection training paradigm that incorporates generated dataset groups, an attention module, and a meta-heuristic algorithm. Experimental results on six medical image datasets compared with ten state-of-the-art methods demonstrate that the LCaM-CNX trained following the proposed paradigm achieves the best performance on five datasets and competitive performance on the other dataset. Notably, our method outperforms the state-of-the-art methods more when the data distribution is more imbalanced.