Flaviane Cristina Rocha Cesar, Lizete Malagoni de Almeida Cavalcante Oliveira, Mariana Cristyen Galvão, Ana Luiza Andrade Lacerda
How Do Healthcare Professionals Develop the Communication Process to Promote Patients' Health Literacy?
International journal of environmental research and public health
chest xray imaging based abnormality localization essential diagnosing various diseases faces significant clinical challenges due complex interpretations growing workload radiologists recent advances deep learning offer promising solutions still critical issue domain inconsistency crossdomain transfer learning hampers efficiency accuracy diagnostic processes study aims address domain inconsistency problem improve autonomic abnormality localization performance heterogeneous chest xray image analysis particularly detecting abnormalities developing selfsupervised learning strategy called barlwotwinscxr utilized two publicly available datasets nih chest xray dataset vindrcxr barlowtwinscxr approach conducted twostage training process initially selfsupervised pretraining performed using adjusted barlow twins algorithm nih dataset resnet50 backbone pretrained imagenet followed supervised finetuning vindrcxr dataset using faster rcnn feature pyramid network fpn study employed mean average precision map intersection union iou 50 area curve auc performance evaluation experiments showed significant improvement model performance barlowtwinscxr approach achieved 3 increase map50 accuracy compared traditional imagenet pretrained models addition ablation cam method revealed enhanced precision localizing chest abnormalities study involved 112120 images nih dataset 18000 images vindrcxr dataset indicating robust training testing samples barlowtwinscxr significantly enhances efficiency accuracy chest xray imagebased abnormality localization outperforming traditional transfer learning methods effectively overcoming domain inconsistency crossdomain scenarios experiment results demonstrate potential using selfsupervised learning improve generalizability models medical settings limited amounts heterogeneous data approach instrumental aiding radiologists particularly highworkload environments offering promising direction future aidriven healthcare solutions