# Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes

Source: [Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes](<https://www.sota2.com/research/paper/68fa62b61e209131ce9fabca>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:25:18.953Z

Authors: Afonso Menegola, Michel Fornaciali, Ramon Pires, Sandra Avila, Eduardo Valle

Published: 2016-09-05T17:31:15.000Z

## Abstract

Deep learning is the current bet for image classification. Its greed for huge amounts of annotated data limits its usage in medical imaging context. In this scenario transfer learning appears as a prominent solution. In this report we aim to clarify how transfer learning schemes may influence classification results. We are particularly focused in the automated melanoma screening problem, a case of medical imaging in which transfer learning is still not widely used. We explored transfer with and without fine-tuning, sequential transfers and usage of pre-trained models in general and specific datasets. Although some issues remain open, our findings may drive future researches.

## 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](<https://arxiv.org/abs/1609.01228>)

[PDF](<https://arxiv.org/pdf/1609.01228.pdf>)