Multi-Scale Vision Transformer with Dynamic Multi-Loss Function for Medical Image Retrieval and Classification  

作　　者：Omar Alqahtani Mohamed Ghouse Asfia Sabahath Omer Bin Hussain Arshiya Begum 

机构地区：[1]Department of Computer Science,College of Computer Science,King Khalid University,Abha,61421,Saudi Arabia

出　　处：《Computers, Materials & Continua》2025年第5期2221-2244,共24页计算机、材料和连续体(英文)

基　　金：funded by the Deanship of Research and Graduate Studies at King Khalid University through small group research under grant number RGP1/278/45.

摘　　要：This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss function.The multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features,while the dynamic loss function adapts during training to optimize classification accuracy and retrieval performance.Our approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets,yielding notable improvements.Specifically,on the ISIC-2018 dataset,our method achieves an F1-Score improvement of+4.84% compared to the standard ViT,with a precision increase of+5.46% for melanoma(MEL).On the ChestX-ray14 dataset,the method delivers an F1-Score improvement of 5.3%over the conventional ViT,with precision gains of+5.0% for pneumonia(PNEU)and+5.4%for fibrosis(FIB).Experimental results demonstrate that our approach outperforms traditional CNN-based models and existing ViT variants,particularly in retrieving relevant medical cases and enhancing diagnostic accuracy.These findings highlight the potential of the proposedmethod for large-scalemedical image analysis,offering improved tools for clinical decision-making through superior classification and case comparison.

关 键 词：Medical image retrieval vision transformer multi-scale encoding multi-loss function ISIC-2018 ChestX-ray14 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]