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From the 1 of 10 linked papers with an AI index.

activity
20242026
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10 papers

physics.med-ph2026

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing

Arman Rahmim, Nourhan Bayasi, Xiaoxiao Li +2

The paper argues that the limited clinical impact of AI in medical imaging stems from a misalignment between how AI systems are built and evaluated and how clinicians make decision…

cs.CV2026

Representation-Level Adversarial Regularization for Clinically Aligned Multitask Thyroid Ultrasound Assessment

Dina Salama, Mohamed Mahmoud, Nourhan Bayasi +2

Thyroid ultrasound is the first-line exam for assessing thyroid nodules and determining whether biopsy is warranted. In routine reporting, radiologists produce two coupled outputs:…

cs.CV2026

Multi-Kernel Gated Decoder Adapters for Robust Multi-Task Thyroid Ultrasound under Cross-Center Shift

Maziar Sabouri, Nourhan Bayasi, Arman Rahmim

Thyroid ultrasound (US) automation couples two competing requirements: global, geometry-driven reasoning for nodule delineation and local, texture-driven reasoning for malignancy r…

eess.IV2025

LesionGen: A Concept-Guided Diffusion Model for Dermatology Image Synthesis

Jamil Fayyad, Nourhan Bayasi, Ziyang Yu +1

Deep learning models for skin disease classification require large, diverse, and well-annotated datasets. However, such resources are often limited due to privacy concerns, high an…

cs.CV2025

Foundation Models as Class-Incremental Learners for Dermatological Image Classification

Mohamed Elkhayat, Mohamed Mahmoud, Jamil Fayyad +1

Class-Incremental Learning (CIL) aims to learn new classes over time without forgetting previously acquired knowledge. The emergence of foundation models (FM) pretrained on large d…

cs.LG2025

Conformal-in-the-Loop for Learning with Imbalanced Noisy Data

John Brandon Graham-Knight, Jamil Fayyad, Nourhan Bayasi +2

Class imbalance and label noise are pervasive in large-scale datasets, yet much of machine learning research assumes well-labeled, balanced data, which rarely reflects real world c…