Dataset Overview

Research Objective

This study systematically evaluates the diagnostic accuracy of various convolutional neural network (CNN) architectures on skin‑lesion images, with particular attention to how demographic parameters such as gender influence performance.

Dataset Construction

• A balanced test set was used.
• Five training sets of equal size were built with female‑to‑male ratios of: all‑female, 75:25, 50:50, 25:75, all‑male.
• All six datasets maintain a 50:50 benign‑to‑malignant ratio.

Data Source

The dataset comprises metadata from the ISIC Archive, with references:

1. Codella, N., et al. (2019)
2. Codella, N.C.F., et al. (2018)
3. Combalia, M., et al. (2019)
4. Gutman, D., et al. (2016)
5. Tschandl, P., et al. (2018)
6. Veronica, R., et al. (2021)

Code Structure

• 0_data: Collected skin‑lesion metadata.
• 1_code: Baseline and multi‑task models, experiment definitions, and MATLAB code.
  • single task: 0_baseline.py (Keras/TensorFlow)
  • reinforcing: 1_mtl_strengthen.py (Keras/TensorFlow)
  • adversarial: br‑net.py (PyTorch)
  • MATLAB folder: Linear‑programming model for creating dataset distributions.
  • Experiments folder: Runs various model‑dataset combinations.
    • e1: 50F:50M (run‑e1: base, run‑e1m: reinforcing, run‑e1br: adversarial)
    • e5: all‑female
    • e7: all‑male
    • e8: 25F:75M
    • e9: 75F:25M