000015453 001__ 15453
000015453 005__ 20250218143335.0
000015453 022__ $$a2948-2925
000015453 0247_ $$2DOI$$a10.1007/s10278-025-01386-w
000015453 037__ $$aARTICLE
000015453 039_9 $$a2025-02-18 14:33:35$$b1000882$$c2025-01-30 13:43:51$$d0$$c2025-01-30 12:57:26$$d1002029$$c2025-01-29 15:00:22$$d0$$y2025-01-29 15:00:13$$z1000262
000015453 041__ $$aeng
000015453 245__ $$aWound segmentation with U-Net using a dual attention mechanism and transfer learning
000015453 260__ $$aÉtats-Unis$$bSpringer
000015453 269__ $$a2025-01
000015453 300__ $$a15 p.
000015453 506__ $$avisible
000015453 520__ $$9eng$$aAccurate wound segmentation is crucial for the precise diagnosis and treatment of various skin conditions through image analysis. In this paper, we introduce a novel dual attention U-Net model designed for precise wound segmentation. Our proposed architecture integrates two widely used deep learning models, VGG16 and U-Net, incorporating dual attention mechanisms to focus on relevant regions within the wound area. Initially trained on diabetic foot ulcer images, we fine-tuned the model to acute and chronic wound images and conducted a comprehensive comparison with other state-of-the-art models. The results highlight the superior performance of our proposed dual attention model, achieving a Dice coefficient and IoU of 94.1% and 89.3%, respectively, on the test set. This underscores the robustness of our method and its capacity to generalize effectively to new data.
000015453 540__ $$acorrect
000015453 592__ $$aHEdS - Genève
000015453 592__ $$aHEPIA - Genève
000015453 592__ $$bInstitut de Recherche de la Haute école de santé de Genève IR-HEdS
000015453 592__ $$cIngénierie et Architecture
000015453 592__ $$cSanté
000015453 6531_ $$9eng$$aU-Net
000015453 6531_ $$9eng$$aattention networks
000015453 6531_ $$9eng$$awound segmentation
000015453 6531_ $$9eng$$amedical imaging
000015453 6531_ $$9eng$$adeep learning
000015453 655__ $$ascientifique
000015453 700__ $$aNiri, Rania$$uComputer Science Department, University of Geneva, Geneva, Switzerland
000015453 700__ $$aZahia, Sofia$$uimito AG, Zurich, Switzerland
000015453 700__ $$aStefanelli, Alessio$$uGeneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland
000015453 700__ $$aSharma, Kaushal$$uComputer Science Department, University of Geneva, Geneva, Switzerland
000015453 700__ $$aProbst, Sebastian$$uGeneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland ; Care Directorate, Geneva University Hospitals, Geneva, Switzerland
000015453 700__ $$aPichon, Swann$$uGeneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland ; Institute of Industrial and IT Engineering, HEPIA, HES-SO Geneva University of Applied Sciences and Arts, Western Switzerland, Geneva, Switzerland
000015453 700__ $$aChanel, Guillaume$$uInstitute of Industrial and IT Engineering, HES-SO Geneva University of Applied Sciences and Arts, Western Switzerland, Geneva (HEPIA), Switzerland ; Computer Science Department, University of Geneva, Geneva, Switzerland
000015453 773__ $$tJournal of imaging informatics in medicine$$jJanuary 2025, to be published
000015453 8564_ $$uhttps://arodes.hes-so.ch/record/15453/files/Probst_2025_wound_segmentation_transfert_learning.pdf$$yPublished version$$93387e19d-97dd-4d63-960c-89f935cdec01$$s1255270
000015453 906__ $$aGOLD
000015453 909CO $$ooai:hesso.tind.io:15453$$pGLOBAL_SET
000015453 950__ $$aSan2
000015453 980__ $$ascientifique
000015453 981__ $$ascientifique