Semi-supervised skin cancer diagnosis based on self-feedback threshold learning
To address the challenges associated with the need for a large amount of annotated data in supervised skin cancer diagnosis models,such as the high cost,time consumption,and fatigue experienced by medical experts during annotation,this study proposes a semi-supervised skin cancer diagnosis method based on Self-Feedback Threshold Learning(SFTL).Building upon the ResNet network pre-trained with labeled data,a global and local class pseudo-label self-feedback threshold learning mechanism is introduced to dynamically select unlabeled samples with ResNet prediction probabilities exceeding the self-feedback threshold.Unsupervised threshold learning loss and classification cross-entropy loss are incorporated for model training,thereby deeply mining the diagnostic information from unlabeled data when labeled samples are scarce and significantly reducing the misdiagnosis rate in unlabeled skin lesion images.Experimental validation was conducted using the publicly available HAM10000 skin lesion dataset,achieving an accuracy of 0.8229 and an F1 score of 0.7651 with only 50％of the data labeled.The results demonstrate that the proposed SFTL model effectively addresses the skin cancer diagnosis task in semi-supervised scenarios and outperforms other compared methods in terms of classification performance.
semi-supervised skin cancer diagnosisself-feedback threshold learningconvolutional neural networksemi-supervised learning
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