Arnaud Delafontaine, Gabriel Saiydoun, Jérôme Frigout, Laurent Fabeck, Olivier Degrenne, François-Régis Sarhan
Pedagogical impact of integration of musculoskeletal anatomy blended learning on physiotherapy education
Frontiers in medicine
worldwide skin cancer prevalence necessitates accurate diagnosis alleviate public health burdens although application artificial intelligence image analysis pattern recognition improved accuracy efficiency early skin cancer diagnosis existing supervised learning methods limited due reliance large amount labeled data overcome limitations data labeling enhance performance diagnostic models study proposes semisupervised skin cancer diagnostic model based selffeedback threshold focal learning stfl capable utilizing partial labeled large scale unlabeled medical images training models unseen scenarios proposed model dynamically adjusts selection threshold unlabeled samples training effectively filtering reliable unlabeled samples using focal learning mitigate impact class imbalance training study experimentally validated ham10000 dataset includes images various types skin lesions experiments conducted across different scales labeled samples 500 annotated samples model demonstrates robust performance 077 accuracy 06408 kappa 077 recall 07426 precision 07462 f1score showcasing efficiency limited labeled data comprehensive testing validates semisupervised models significant advancements diagnostic accuracy efficiency underscoring value integrating unlabeled data model offers new perspective medical image processing contributes robust scientific support early diagnosis treatment skin cancer