# Knowledge Transfer for Melanoma Screening with Deep Learning

arXiv: https://arxiv.org/abs/1703.07479 · HTML: https://mlsanity.com/paper/1703.07479/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2017.

## Built on by (most cited first, 19 of 19)

- [Big Self-Supervised Models Advance Medical Image Classification](https://mlsanity.com/paper/2101.05224/) (2021)
- [Robust and Efficient Medical Imaging with Self-Supervision](https://mlsanity.com/paper/2205.09723/) (2022)
- [Supervised Transfer Learning at Scale for Medical Imaging](https://mlsanity.com/paper/2101.05913/) (2021)
- [GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review](https://mlsanity.com/paper/2104.10603/) (2021)
- [Partial transfusion: on the expressive influence of trainable batch norm parameters for transfer learning](https://mlsanity.com/paper/2102.05543/) (2021)
- [An Evaluation of Self-Supervised Pre-Training for Skin-Lesion Analysis](https://mlsanity.com/paper/2106.09229/) (2021)
- [Full or Weak annotations? An adaptive strategy for budget-constrained annotation campaigns](https://mlsanity.com/paper/2303.11678/) (2023)
- [Transfer learning for time series classification using synthetic data generation](https://mlsanity.com/paper/2207.07897/) (2022)
- [Self-Supervised Learning for Segmentation](https://mlsanity.com/paper/2101.05456/) (2021)
- [Self-Supervised Endoscopic Image Key-Points Matching](https://mlsanity.com/paper/2208.11424/) (2022)
- [Context-Aware Self-Supervised Learning of Whole Slide Images](https://mlsanity.com/paper/2306.04763/) (2023)
- [DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification](https://mlsanity.com/paper/2308.00475/) (2023)
- [Assessing the Generalizability of Deep Neural Networks-Based Models for Black Skin Lesions](https://mlsanity.com/paper/2310.00517/) (2023)
- [Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification](https://mlsanity.com/paper/2107.05940/) (2021)
- [Robust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learning](https://mlsanity.com/paper/2211.08559/) (2022)
- [Self-Supervised Multi-Modality Learning for Multi-Label Skin Lesion Classification](https://mlsanity.com/paper/2310.18583/) (2023)
- [Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors](https://mlsanity.com/paper/2112.03032/) (2021)
- [A Light-weight Transformer-based Self-supervised Matching Network for Heterogeneous Images](https://mlsanity.com/paper/2404.19311/) (2024)
- [Intuitions of Machine Learning Researchers about Transfer Learning for Medical Image Classification](https://mlsanity.com/paper/2510.00902/) (2025)

## Abstract (arXiv)

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.