1 citations · 1 across the 10 of their papers we have counts for
10 papers
Human-Centered Explainable AI for TinyML Edge Devices: A Pareto-Based Selection Framework with LLM-Guided Design
Zeinab Dehghani, Dhavalkumar Thakker, Koorosh Aslansefat +4
Edge Artificial Intelligence (Edge AI) enables the deployment of AI models directly on local edge devices, while such deployments are subject to strict resource constraints, partic…
Data Safety: Synthetic Data Quality Analysis Using CIFAKE Dataset
Kuniko Paxton, Amila Akagić, Koorosh Aslansefat +2
Recently, the societal implementation of high-performance image classification models has expanded rapidly. While these models require vast amounts of training data to improve perf…
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…
EcoFair: Energy-Efficient Inference Routing for Edge AI under Data Degradation
Mostafa Anoosha, Dhavalkumar Thakker, Kuniko Paxton +4
Medical edge-AI systems must operate under a difficult tension: delivering reliable diagnostic inference while running on devices with limited battery capacity, memory, and compute…
HybridVFL: Disentangled Feature Learning for Edge-Enabled Vertical Federated Multimodal Classification
Mostafa Anoosha, Zeinab Dehghani, Kuniko Paxton +2
Vertical Federated Learning (VFL) offers a privacy-preserving paradigm for Edge AI scenarios like mobile health diagnostics, where sensitive multimodal data reside on distributed,…
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…
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…
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…
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…
Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression
Kuniko Paxton, Koorosh Aslansefat, Dhavalkumar Thakker +1
Fairness is a critical component of Trustworthy AI. In this paper, we focus on Machine Learning (ML) and the performance of model predictions when dealing with skin color. Unlike o…