Affiliations: College of Biomedical Engineering, Sichuan University, Chengdu, Sichuan, China
Correspondence:
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Correspondence to: Jiangli Lin, College of Biomedical Engineering, Sichuan University, Chengdu, Sichuan 610065, China. E-mail: 57163708@qq.com; ORCID: https://orcid.org/0000-0003-2030-9681
Abstract: BACKGROUND:Melanoma is a tumor caused by melanocytes with a high degree of malignancy, easy local recurrence, distant metastasis, and poor prognosis. It is also difficult to be detected by inexperienced dermatologist due to their similar appearances, such as color, shape, and contour. OBJECTIVE:To develop and test a new computer-aided diagnosis scheme to detect melanoma skin cancer. METHODS:In this new scheme, the unsupervised clustering based on deep metric learning is first conducted to make images with high similarity together and the corresponding model weights are utilized as teacher-model for the next stage. Second, benefit from the knowledge distillation, the attention transfer is adopted to make the classification model enable to learn the similarity features and information of categories simultaneously which improve the diagnosis accuracy than the common classification method. RESULTS:In validation sets, 8 categories were included, and 2443 samples were calculated. The highest accuracy of the new scheme is 0.7253, which is 5% points higher than the baseline (0.6794). Specifically, the F1-Score of three malignant lesions BCC (Basal cell carcinoma), SCC (Squamous cell carcinomas), and MEL (Melanoma) increase from 0.65 to 0.73, 0.28 to 0.37, and 0.54 to 0.58, respectively. In two test sets of HAN including 3844 samples and BCN including 6375 samples, the highest accuracies are 0.68 and 0.53 for HAM and BCN datasets, respectively, which are higher than the baseline (0.649 and 0.516). Additionally, F1 scores of BCC, SCC, MEL are 0.49, 0.2, 0.45 in HAM dataset and 0.6, 0.14, 0.55 in BCN dataset, respectively, which are also higher than F1 scores the results of baseline. CONCLUSIONS:This study demonstrates that the similarity clustering method enables to extract the related feature information to gather similar images together. Moreover, based on the attention transfer, the proposed classification framework can improve total accuracy and F1-score of skin lesion diagnosis.
