# Knowledge Transfer for Melanoma Screening with Deep Learning Source: [Knowledge Transfer for Melanoma Screening with Deep Learning]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:58:11.540Z Authors: Afonso Menegola, Michel Fornaciali, Ramon Pires, Fl\\'avia Vasques Bittencourt, Sandra Avila, Eduardo Valle Published: 2017-03-22T00:51:14.000Z ## Abstract Knowledge transfer impacts the performance of deep learning -- the state of the art for image classification tasks, including automated melanoma screening. Deep learning's greed for large amounts of training data poses a challenge for medical tasks, which we can alleviate by recycling knowledge from models trained on different tasks, in a scheme called transfer learning. Although much of the best art on automated melanoma screening employs some form of transfer learning, a systematic evaluation was missing. Here we investigate the presence of transfer, from which task the transfer is sourced, and the application of fine tuning (i.e., retraining of the deep learning model after transfer). We also test the impact of picking deeper (and more expensive) models. Our results favor deeper models, pre-trained over ImageNet, with fine-tuning, reaching an AUC of 80.7% and 84.5% for the two skin-lesion datasets evaluated. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()