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cs.CV2026

Data Safety: Synthetic Data Quality Analysis Using CIFAKE Dataset

Kuniko Paxton, Amila Akagić, Koorosh Aslansefat +2

The paper examines how synthetic images generated by different methods differ from real images in feature space, color statistics, and model training, and proposes strategies for e…

cs.CV2026

Exploring the Impact of Skin Color on Skin Lesion Segmentation

Kuniko Paxton, Medina Kapo, Amila Akagić +3

Skin cancer, particularly melanoma, remains a major cause of morbidity and mortality, making early detection critical. AI-driven dermatology systems often rely on skin lesion segme…

cs.CV2025

Skewness-Guided Pruning of Multimodal Swin Transformers for Federated Skin Lesion Classification on Edge Devices

Kuniko Paxton, Koorosh Aslansefat, Dhavalkumar Thakker +1

In recent years, high-performance computer vision models have achieved remarkable success in medical imaging, with some skin lesion classification systems even surpassing dermatolo…

cs.CV2025

Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting

Kuniko Paxton, Zeinab Dehghani, Koorosh Aslansefat +2

Skin color has historically been a focal point of discrimination, yet fairness research in machine learning for medical imaging often relies on coarse subgroup categories, overlook…

cs.CV2025

Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning

Kuniko Paxton, Koorosh Aslansefat, Dhavalkumar Thakker +2

Recent advances in deep learning have significantly improved the accuracy of skin lesion classification models, supporting medical diagnoses and promoting equitable healthcare. How…

cs.CV2025

Safer Skin Lesion Classification with Global Class Activation Probability Map Evaluation and SafeML

Kuniko Paxton, Koorosh Aslansefat, Amila Akagić +2

Recent advancements in skin lesion classification models have significantly improved accuracy, with some models even surpassing dermatologists' diagnostic performance. However, in…