Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
DFCA: Disentangled Feature Contrastive Learning and Augmentation for Fairer Dermatological Diagnostics
Authors: Pengcheng Zhao, Xiaowei Ding
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that DFCA significantly improves both fairness and accuracy compared to state-of-the-art methods. Extensive experiments on two datasets shows that DFCA, by combing disentangled feature contrastive learning and augmentation, improves both fairness and accuracy compared to SOTA methods. |
| Researcher Affiliation | Academia | Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China EMAIL |
| Pseudocode | No | The paper describes the proposed DFCA framework in detail through textual descriptions and a diagram (Figure 1), but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states, "We implement our DFCA model by Py Torch," but it does not provide any specific links to a code repository, an explicit statement about code release, or mention code in supplementary materials. |
| Open Datasets | Yes | We use two well-known dermatology datasets to evaluate our proposed method: Fitzpatrick-17k dataset [Groh et al., 2021] and DDI dataset [Daneshjou et al., 2022]. Both of the datasets contain skin tone attribute. |
| Dataset Splits | No | The paper mentions training for a certain number of epochs and discusses 'in-domain' and 'out-domain' experiments, but it does not provide specific details on how the datasets (Fitzpatrick-17k and DDI) were split into training, validation, and test sets (e.g., percentages, exact counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | We implement our DFCA model by Py Torch. The paper mentions PyTorch but does not provide a specific version number for it or any other software dependencies. |
| Experiment Setup | Yes | DFCA is trained for 150 epochs firstly with the real datasets and 100 epochs with the mixture of feature augmentation. Our model is trained by Adam optimizer with a learning rate lr = 0.0001. The batch size is 32. The weights are α = 10, β = 0.5 and γ = 1. |