7 papers
Expert-Guided Explainable Few-Shot Learning with Active Sample Selection for Medical Image Analysis
Longwei Wang, Ifrat Ikhtear Uddin, KC Santosh
Medical image analysis faces two critical challenges: scarcity of labeled data and lack of model interpretability, both hindering clinical AI deployment. Few-shot learning (FSL) ad…
Explainability-Guided Defense: Attribution-Aware Model Refinement Against Adversarial Data Attacks
Longwei Wang, Mohammad Navid Nayyem, Abdullah Al Rakin +3
The growing reliance on deep learning models in safety-critical domains such as healthcare and autonomous navigation underscores the need for defenses that are both robust to adver…
Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustness
Longwei Wang, Ifrat Ikhtear Uddin, KC Santosh +3
Adversarial examples reveal critical vulnerabilities in deep neural networks by exploiting their sensitivity to imperceptible input perturbations. While adversarial training remain…
Toward Carbon-Neutral Human AI: Rethinking Data, Computation, and Learning Paradigms for Sustainable Intelligence
KC Santosh, Rodrigue Rizk, Longwei Wang
The rapid advancement of Artificial Intelligence (AI) has led to unprecedented computational demands, raising significant environmental and ethical concerns. This paper critiques t…
Promoting Shape Bias in CNNs: Frequency-Based and Contrastive Regularization for Corruption Robustness
Robin Narsingh Ranabhat, Longwei Wang, Amit Kumar Patel +1
Convolutional Neural Networks (CNNs) excel at image classification but remain vulnerable to common corruptions that humans handle with ease. A key reason for this fragility is thei…
Expert-Guided Explainable Few-Shot Learning for Medical Image Diagnosis
Ifrat Ikhtear Uddin, Longwei Wang, KC Santosh
Medical image analysis often faces significant challenges due to limited expert-annotated data, hindering both model generalization and clinical adoption. We propose an expert-guid…