DFCA: Disentangled Feature Contrastive Learning and Augmentation for Fairer Dermatological Diagnostics
Authors: Pengcheng Zhao, Xiaowei Ding
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | 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. |