8 citations · 24 across the 26 of their papers we have counts for
27 papers
GazeRefine: Expert Gaze as a Test-Time Prompt for Training-Free Medical Image Segmentation
Mohammed Oussama Benyahia, Marouane Tliba, Mohamed Amine Kerkouri +10
Medical image segmentation remains difficult to scale because high-performing methods typically rely on dense expert annotations and task-specific training. We introduce GazeRefine…
Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans
Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani +2
Human visual search is serial: the fovea must land on a candidate to confirm it, and those landings form a scanpath. Whether multimodal large language models (MLLMs), given the sam…
Divergent Gaze Patterns in Artistic Viewing: Spatial and Temporal Signatures of Attention Across Autistic Individuals, Artists, and Neurotypical Observers
Mohammed Amine Kerkouri, Daphné Senggaran, Renaud Jusiak +7
How different populations visually explore artworks bears on cognitive science and on accessibility design, yet most eye-tracking work in autism has used social scenes rather than…
ThinkProbe: Beyond Accuracy -- Structural Profiling of Open-Ended LLM Reasoning Traces via Non-Generative Thought Graphs
Mohamed Amine Kerkouri, Simon D. Hernandez, Marouane Tliba +3
We present ThinkProbe, a framework for structural analysis of LLM reasoning traces. ThinkProbe converts each trace into a Thought Graph a directed graph with cycles, 8 node types,…
CT-DegradBench: A Physics-Informed Benchmark for CT Degradation Detection and Severity Estimation
Yousra Nabila Taifour, Marouane Tliba, Zuheng Ming +9
Computed tomography (CT) images are frequently degraded by acquisition artifacts, including noise, blur, streaking, aliasing, and metal artifacts. Yet CT enhancement is still large…
GazeVaLM: A Multi-Observer Eye-Tracking Benchmark for Evaluating Clinical Realism in AI-Generated X-Rays
David Wong, Zeynep Isik, Bin Wang +22
We introduce GazeVaLM, a public eye-tracking dataset for studying clinical perception during chest radiograph authenticity assessment. The dataset comprises 960 gaze recordings fro…
Progressive Deep Learning for Automated Spheno-Occipital Synchondrosis Maturation Assessment
Omid Halimi Milani, Amanda Nikho, Marouane Tliba +4
Accurate assessment of spheno-occipital synchondrosis (SOS) maturation is a key indicator of craniofacial growth and a critical determinant for orthodontic and surgical timing. How…
What They Saw, Not Just Where They Looked: Semantic Scanpath Similarity via VLMs and NLP metric
Mohamed Amine Kerkouri, Marouane Tliba, Bin Wang +3
Scanpath similarity metrics are central to eye-movement research, yet existing methods predominantly evaluate spatial and temporal alignment while neglecting semantic equivalence b…
VP-Hype: A Hybrid Mamba-Transformer Framework with Visual-Textual Prompting for Hyperspectral Image Classification
Abdellah Zakaria Sellam, Fadi Abdeladhim Zidi, Salah Eddine Bekhouche +4
Accurate classification of hyperspectral imagery (HSI) is often frustrated by the tension between high-dimensional spectral data and the extreme scarcity of labeled training sample…
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.…
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…
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…
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…
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…
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…
Shifting Focus: From Global Semantics to Local Prominent Features in Swin-Transformer for Knee Osteoarthritis Severity Assessment
Aymen Sekhri, Marouane Tliba, Mohamed Amine Kerkouri +4
Conventional imaging diagnostics frequently encounter bottlenecks due to manual inspection, which can lead to delays and inconsistencies. Although deep learning offers a pathway to…
Quantization Effects on Neural Networks Perception: How would quantization change the perceptual field of vision models?
Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani +1
Neural network quantization is a critical technique for deploying models on resource-limited devices. Despite its widespread use, the impact of quantization on model perceptual fie…
Insights into Classifying and Mitigating LLMs' Hallucinations
Alessandro Bruno, Pier Luigi Mazzeo, Aladine Chetouani +2
The widespread adoption of large language models (LLMs) across diverse AI applications is proof of the outstanding achievements obtained in several tasks, such as text mining, text…
Automatic diagnosis of knee osteoarthritis severity using Swin transformer
Aymen Sekhri, Marouane Tliba, Mohamed Amine Kerkouri +4
Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. Early detection and diagnosis are crucial for successful clinical i…
Quality evaluation of point clouds: a novel no-reference approach using transformer-based architecture
Marouane Tliba, Aladine Chetouani, Giuseppe Valenzise +1
With the increased interest in immersive experiences, point cloud came to birth and was widely adopted as the first choice to represent 3D media. Besides several distortions that c…
PCQA-GRAPHPOINT: Efficients Deep-Based Graph Metric For Point Cloud Quality Assessment
Marouane Tliba, Aladine Chetouani, Giuseppe Valenzise +1
Following the advent of immersive technologies and the increasing interest in representing interactive geometrical format, 3D Point Clouds (PC) have emerged as a promising solution…
An Inter-observer consistent deep adversarial training for visual scanpath prediction
Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani +1
The visual scanpath is a sequence of points through which the human gaze moves while exploring a scene. It represents the fundamental concepts upon which visual attention research…
Deep-based quality assessment of medical images through domain adaptation
Marouane Tliba, Aymen Sekhri, Mohamed Amine Kerkouri +1
Predicting the quality of multimedia content is often needed in different fields. In some applications, quality metrics are crucial with a high impact, and can affect decision maki…
A domain adaptive deep learning solution for scanpath prediction of paintings
Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani +1
Cultural heritage understanding and preservation is an important issue for society as it represents a fundamental aspect of its identity. Paintings represent a significant part of…