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Clinical Melanoma Diagnosis with Artificial Intelligence: Insights from a Prospective Multicenter Study
Early detection of melanoma, a potentially lethal type of skin cancer with high prevalence worldwide, improves patient prognosis.
Mitigating the Influence of Domain Shift in Skin Lesion Classification: A Benchmark Study of Unsupervised Domain Adaptation Methods on Dermoscopic Images
However, small or heavily imbalanced datasets lead to a reduced conformity of the results due to the influence of these factors on the methods performance.
Using Multiple Dermoscopic Photographs of One Lesion Improves Melanoma Classification via Deep Learning: A Prognostic Diagnostic Accuracy Study
Classifier performance was measured using area under the receiver operating characteristic curve (AUROC), expected calibration error (ECE) and maximum confidence change (MCC) for (I) a single-view scenario, (II) a multiview scenario using multiple artificially modified images per lesion and (III) a multiview scenario with multiple real-world images per lesion.
Domain shifts in dermoscopic skin cancer datasets: Evaluation of essential limitations for clinical translation
The limited ability of Convolutional Neural Networks to generalize to images from previously unseen domains is a major limitation, in particular, for safety-critical clinical tasks such as dermoscopic skin cancer classification.
