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20202026
most citedInsights into Classifying and Mitigating LLMs' Hallucinations

8 citations · 24 across the 26 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.CV2025

Morphology-Aware KOA Classification: Integrating Graph Priors with Vision Models

Marouane Tliba, Mohamed Amine Kerkouri, Yassine Nasser +4

Knee osteoarthritis (KOA) diagnosis from radiographs remains challenging due to the subtle morphological details that standard deep learning models struggle to capture effectively.…

cs.CV2025

Knowledge Distillation Approach for SOS Fusion Staging: Towards Fully Automated Skeletal Maturity Assessment

Omid Halimi Milani, Amanda Nikho, Marouane Tliba +3

We introduce a novel deep learning framework for the automated staging of spheno-occipital synchondrosis (SOS) fusion, a critical diagnostic marker in both orthodontics and forensi…

cs.CV2025

Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality

Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani +2

This position paper argues that Mean Opinion Score (MOS), while historically foundational, is no longer sufficient as the sole supervisory signal for multimedia quality assessment…

eess.IV2025

Gradient Attention Map Based Verification of Deep Convolutional Neural Networks with Application to X-ray Image Datasets

Omid Halimi Milani, Amanda Nikho, Lauren Mills +3

Deep learning models have great potential in medical imaging, including orthodontics and skeletal maturity assessment. However, applying a model to data different from its training…

cs.CV2025

Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images

David C Wong, Bin Wang, Gorkem Durak +21

Eye-tracking analysis plays a vital role in medical imaging, providing key insights into how radiologists visually interpret and diagnose clinical cases. In this work, we first ana…

cs.CV2025★ 1 cited

Eyes Tell the Truth: GazeVal Highlights Shortcomings of Generative AI in Medical Imaging

David Wong, Bin Wang, Gorkem Durak +23

The demand for high-quality synthetic data for model training and augmentation has never been greater in medical imaging. However, current evaluations predominantly rely on computa…