Aymen SEKHRI

Aymen SEKHRI

Ph.D. candidate in machine learning for medical imaging and immersive media

Publications

You can also find my articles on my Google Scholar profile.

Shifting Focus: From Global Semantics to Local Prominent Features in Swin Transformers for Knee Osteoarthritis Severity Assessment

Published in 32nd European Signal Processing Conference (EUSIPCO 2024), 2024

Conventional imaging diagnostics frequently encounter bottlenecks due to manual inspection, which can lead to delays and inconsistencies. Although deep learning offers a pathway to automation and enhanced accuracy, foundational models in computer vision often emphasise global context at the expense of the local structures that clinicians rely on.

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Do Digital Images Tell the Truth?

Published in Digital Image Security, 2024

Since the advent of digital cameras, image editing tools have made it straightforward to manipulate content. Copy-move forgeries—where a region is duplicated and pasted within the same frame—are particularly challenging for human observers to detect, yet they threaten the credibility of visual evidence.

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Deep-Based Quality Assessment of Medical Images Through Domain Adaptation

Published in IEEE International Conference on Image Processing (ICIP 2022), 2022

Predicting the perceptual quality of medical imagery is crucial for diagnostic workflows, yet annotated data are scarce and quality metrics must remain reliable across acquisition domains. We introduce a lightweight convolutional self-attention architecture that interpolates global quality scores from local perceptual cues while remaining data efficient.

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