Multimedia Interaction and Communication Lab AAST

We are pleased to announce our latest preprint, introducing PAIRED — our proposed framework for transparent reporting of AI contributions in scientific research.

In this work, we define a process-anchored approach for attributing AI contributions, something that is entirely absent from current disclosure frameworks. Built around a four-stage research pipeline, PAIRED defines five principal roles for AI arranged along an agency gradient, a unit of attribution called the decision point, and an artifact-trigger rule that governs when a three-field micro-log entry must be recorded.

We demonstrate PAIRED through three worked examples drawn from a recent preprint from our lab, and we close with an Acknowledgments section that is itself a meta-demonstration of PAIRED — documenting the very article that proposes it using the framework's own micro-log format.

As part of the discussion, we propose a concrete adoption pathway and issue a call-to-action to AI platform developers: integrating PAIRED as an opt-in feature in language models that offer a Deep Research functionality, so that the model assists with bookkeeping while the researcher retains full epistemic authority over the final record.

The preprint is available at: https://arxiv.org/abs/2605.24325

Excited to share our latest preprint on AI-powered swimming analytics and coaching support systems.

In this paper, we propose a novel Retrieval-Augmented Generation (RAG) framework that combines multimodal physiological data, IMU sensing, domain literature, and expert coaching knowledge to synthesize high-quality datasets for trustworthy AI applications in swimming science.

Our work explores how Large Language Models (LLMs) and synthetic data generation can help overcome challenges such as limited aquatic datasets, privacy constraints, and expensive expert labeling—while enabling more context-aware and technically grounded AI support for athletes and coaches.

The study also introduces a multi-agent LLM architecture for generating and validating expert-level “Question–Context–Answer” triplets aligned with evidence-based sports science principles.

We hope this work contributes toward more reliable, explainable, and practically deployable AI systems in sports performance analysis and coaching.

The preprint can be found through the following link:

https://lnkd.in/dHN439de

🚀 Excited to share our latest work, now available as a preprint!

📄 "Do Hybrid CNN–Transformer Architectures Really Generalize? A Systematic Review of Cross-Organ, Multi-Modal, and Multi-Dataset Transfer in Medical Imaging"

As AI moves closer to real-world clinical deployment, generalization — not just performance — becomes the defining challenge. In this systematic review, we synthesize evidence from 71 peer-reviewed studies (2020–2025) to critically examine whether hybrid CNN–Transformer models truly deliver on their generalization promise in medical imaging.

🔍 What we cover:
→ A structural taxonomy of 5 recurring hybrid architectural patterns
→ Generalization across cross-organ, cross-dataset, and multi-modal scenarios
→ A critical assessment of reproducibility and evaluation rigor in the field
→ A forward-looking roadmap for efficiency-aware, clinically deployable AI

💡 Key takeaway: Hybrid models show strong promise, but high computational cost, limited external validation, and over-reliance on fully supervised learning remain significant barriers to clinical translation.

https://lnkd.in/dNWAfEJV

We’re hiring: 1 Part-Time Research Assistant for Research on CNN-Transformer Hybrid Models (3 months, extendable) — MIC Lab (Remote-friendly).

The Multimedia Interaction and Communication (MIC) Lab is opening one part-time RA position for a 3-month research project (extendable) starting immediately. If you’re a fresh graduate eager to build serious, hands-on research experience in medical imaging & AI, we’d love to hear from you.

✅ MIC Lab provides computational resources to support your experiments and training runs.

Must-haves
— Fresh graduate (Bachelor’s or Master’s; Class of 2023–2025).
— Background in computer vision / medical imaging / machine learning.
— Strong proficiency with deep learning frameworks (e.g., PyTorch / TensorFlow).
— Strong reading & synthesis skills (papers → structured insights).
— Excellent academic writing in English.
— Reliable part-time commitment for 3 months; ability to deliver weekly progress.

— Strong experience in CNN-transformer hybrid models AI for classification and/or segmentation, with clear evidence through projects, code, or research outputs.

Nice-to-haves
— Proven experience writing research papers (drafting, revising, responding to feedback).
— Experience with LaTeX and reference managers.

Who should apply
— Fresh graduates seeking rigorous, real-world research experience to launch a career in CV/AI.
— Detail-oriented self-starters who can own tasks end-to-end (reading → experiments → write-ups).

How to apply
Email [email protected] with subject line:
“Part-Time Research Assistant – MIC Lab – Spring 2026”

Include:
1. Your resume
2. GitHub / Google Scholar / portfolio (if available)
3. A link to one recent paper you liked (relevant to the scope of the RA opening) + 1–2 sentences on what it adds

Start date: Immediately (3-month part-time commitment — extendable)
Remote/Location: Remote-friendly; meeting times aligned with GMT +2 / +3
=====
About MIC Lab:
MIC Lab is a research group, a part of Ethri Labs, and spin-off the Arab Academy for Science and Technology (AAST), Egypt. We conduct research on multimedia systems, focusing on n-dimensional signal coding/compression and synthesis, innovative interaction modalities, and HCI-grounded evaluation of intelligent systems and experiences.

If you know someone who fits, please share or tag them 👇

Bridging the Gap Between AI Accuracy and Clinical Trust in Neurovascular Care

Artificial Intelligence is transformative for neuroimaging, but the "black-box" nature of deep learning remains a critical barrier to surgical adoption. If a model flags an intracranial aneurysm as high-risk, a surgeon needs to know why.

In our latest preprint, we introduce an end-to-end 3D Concept Bottleneck Model (CBM) framework that brings human-understandable reasoning to aneurysm classification.

The Impact: Unlike traditional AI that provides post-hoc visual heatmaps, our framework constrains the model’s internal logic to 30 real-world clinical indices—such as vessel geometry and hemodynamics.

Key Results:
- High Performance: Achieved peak diagnostic accuracies of 93.33% (ResNet-34) and 91.43% (DenseNet-121).
- Reliability: Maintained an accuracy-generalization gap of < 0.04, ensuring stable results across diverse clinical data.

Trust by Design: By anchoring AI predictions to neurosurgical principles, we provide clinicians with a verifiable "reasoning" path for better planning.

Worldwide Potential: By prioritizing clinical transparency alongside predictive power, this framework offers a scalable solution for healthcare systems worldwide to adopt AI-assisted diagnostics that physicians can actually trust.

Here is the link to the preprint:
https://lnkd.in/dYaT3hWV

We’re hiring: 2 Part-Time Research Assistants (3 months, extendable) — MIC Lab (Remote-friendly).

The Multimedia Interaction and Communication (MIC) Lab is opening two part-time RA positions for a 3-month research project (extendable) starting immediately. If you’re a fresh graduate eager to build serious, hands-on research experience in medical imaging & AI, we’d love to hear from you.

✅ MIC Lab provides computational resources to support your experiments and training runs.

Must-haves
— Fresh graduate (Bachelor’s or Master’s; Class of 2023–2025).
— Background in computer vision / medical imaging / machine learning.
— Strong proficiency with deep learning frameworks (e.g., PyTorch / TensorFlow).
— Strong reading & synthesis skills (papers → structured insights).
— Excellent academic writing in English.
— Reliable part-time commitment for 3 months; ability to deliver weekly progress.

Two openings (choose your best fit)
1) Generative Modeling Track
— Strong, demonstrable experience with GANs / generative modeling, ideally evidenced by projects, GitHub repos, or publications.

2) Explainability Track (Classification/Segmentation)
— Strong experience in explainable AI for classification and/or segmentation, with clear evidence through projects, code, or research outputs.

Nice-to-haves
— Proven experience writing research papers (drafting, revising, responding to feedback).
— Experience with LaTeX and reference managers.

Who should apply
— Fresh graduates seeking rigorous, real-world research experience to launch a career in CV/AI.
— Detail-oriented self-starters who can own tasks end-to-end (reading → experiments → write-ups).

How to apply
Email [email protected] with subject line:
“Part-Time Research Assistant – MIC Lab – Fall 2025”

Include:
1. Your resume
2. GitHub / Google Scholar / portfolio (if available)
3. A link to one recent paper you liked (relevant to the track you’re applying for) + 1–2 sentences on what it adds

Start date: Immediately (3-month part-time commitment — extendable)
Remote/Location: Remote-friendly; meeting times aligned with GMT +2 / +3
=====
About MIC Lab:

MIC Lab is a research group at the Arab Academy for Science and Technology (AAST), Egypt. We conduct research on multimedia systems, focusing on n-dimensional signal coding/compression and synthesis, innovative interaction modalities, and HCI-grounded evaluation of intelligent systems and experiences.

If you know someone who fits, please share or tag them 👇

Domain shift can make top-performing medical imaging models fail catastrophically on external datasets, hindering trustworthy clinical AI and motivating Explainable AI (XAI) as a diagnostic tool. We present a rigorous two-phase diagnosis of UMamaba (a state-space model) for cerebrovascular segmentation. First, we quantify a domain gap between RSNA CTA Aneurysm (Source) and TopCoW Circle of Willis CT (Target), driven by differences in Z-resolution and background noise, and observe a sharp performance drop. Second, our core contribution uses Seg-XRes-CAM to quantify attention overlap with Ground Truth and predictions, showing attention abandons true anatomy and follows spurious cues. 0.8604 → 0.2902; IoU ≈ 0.101 (GT), IoU ≈ 0.282 (pred), at 0.3 threshold.

Check the preprint through the following link:
http://arxiv.org/abs/2512.13977

This systematic review investigates how integrating wavelet-based pooling mechanisms and other advanced pooling strategies enhance the performance of Generative Adversarial Networks (GANs).

GANs, despite their success in data synthesis, struggle with training instability, mode collapse, and image quality issues. The review examines GAN variants (like DCGAN, StyleGAN, CycleGAN) that incorporate wavelet-based decompositions and various pooling techniques (average, max, adaptive) to address these challenges.

Comparing these enhanced models against traditional GANs, the review assesses improvements in image quality metrics (FID, IS), training stability, feature preservation, and computational efficiency.

The findings suggest that wavelet and advanced pooling techniques lead to significant performance enhancements, including sharper image generation, better texture preservation, reduced mode collapse, and increased robustness. This synthesis highlights architectural trends that improve GAN adaptability and performance across various domains.

https://www.researchsquare.com/article/rs-8321844/v1

In this pre-print, we demonstrate that high-performance, complex tasks like volumetric cerebrovascular segmentation can be achieved using a non-learned, generalized, and highly efficient algorithm (ZFP), directly challenging the dominance of task-specific, deep learning compression methods.

Article:

Efficiency vs. Efficacy: Assessing the Compression Ratio-Dice Score Relationship through a Simple Benchmarking Framework for Cerebrovascular 3D Segmentation The increasing size and complexity of medical imaging datasets, particularly in 3D formats, present significant barriers to collaborative research and transferability. This study investigates whether the ZFP compression technique can mitigate these challenges without compromising the performance of....

We’re hiring a full-time (8 hrs/day) Research Assistant for a 3-month (extendible) research project at the MIC Lab. If you’re a fresh graduate passionate about medical imaging & AI, we’d love to hear from you.

Must-haves:
========
— Fresh graduate (Bachelor’s or Master’s; Class of 2023–2025).
— Background in computer vision / medical imaging / ML.
— Strong proficiency with ML algorithms and deep learning frameworks (e.g., PyTorch/TensorFlow)
— Strong reading & synthesis skills.
— Excellent academic writing in English; commitment to full-time work for 3 months.

Nice-to-haves:
=========
— Proven experience writing research papers (drafting, revising, responding to reviews)
— Familiarity with literature workflows (PRISMA, screening, data extraction).
— Comfortable with basic stats/spreadsheets; organization with Zotero/EndNote/Mendeley.
— Python/R for simple meta-analysis or plotting.
— Prior exposure to domain adaptation / transfer learning / foundation models.
— Experience with LaTeX and reference managers.

Who should apply:
============
— Fresh graduates only, seeking rigorous, real-world research experience to launch a research career in CV/AI.
— Detail-oriented self-starters who can own a literature pipeline end-to-end.

How to apply:
=========
Email [email protected] with the subject line:
Research Assistant – Medical Imaging – Fall 2025
Include your CV, any GitHub/Google Scholar links (if available), and one recent paper you liked (1–2 sentences on what it adds).

Starting Date:
=========
As soon as possible (3-month full-time commitment--extendible).

Remote/Location:
===========
Remote-friendly; meeting times aligned with GMT+2/+3.

𝐂𝐫𝐨𝐬𝐬-𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐒𝐭𝐫𝐞𝐬𝐬 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐧𝐠 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬 𝐨𝐧 𝐇𝐞𝐭𝐞𝐫𝐨𝐠𝐞𝐧𝐞𝐨𝐮𝐬 𝐒𝐭𝐫𝐞𝐬𝐬 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨𝐬 𝐔𝐬𝐢𝐧𝐠 𝐄𝐄𝐆 𝐒𝐢𝐠𝐧𝐚𝐥𝐬

Link: https://www.mdpi.com/2673-2688/6/4/79

Background/Objectives: This article addresses the challenge of stress detection across diverse contexts. Mental stress is a worldwide concern that substantially affects human health and productivity, rendering it a critical research challenge. There has been limited research on assessing ML models trained in one context and utilized in another. The objective of ML-based stress detection systems is to create models that generalize across various contexts.

Methods: This study examines the generalizability of ML models employing EEG recordings from two stress-inducing contexts: mental arithmetic evaluation (MAE) and virtual reality (VR) gaming. We present a data collection workflow and publicly release a portion of the dataset. Furthermore, we evaluate classical ML models and their generalizability, offering insights into the influence of training data on model performance, data efficiency, and related expenses. EEG data were acquired leveraging MUSE-STM hardware during stressful MAE and VR gaming scenarios. The methodology entailed preprocessing EEG signals using wavelet denoising mother wavelets, assessing individual and aggregated sensor data, and employing three ML models—linear discriminant analysis (LDA), support vector machine (SVM), and K-nearest neighbors (KNN)—for classification purposes.

Results: In Scenario 1, where MAE was employed for training and VR for testing, the TP10 electrode attained an average accuracy of 91.42% across all classifiers and participants, whereas the SVM classifier achieved the highest average accuracy of 95.76% across all participants. In Scenario 2, adopting VR data as the training data and MAE data as the testing data, the maximum average accuracy achieved was 88.05% with the combination of TP10, AF8, and TP9 electrodes across all classifiers and participants, whereas the LDA model attained the peak average accuracy of 90.27% among all participants. The optimal performance was achieved with Symlets 4 and Daubechies-2 for Scenarios 1 and 2, respectively.

Conclusions: The results demonstrate that although ML models exhibit generalization capabilities across stressors, their performance is significantly influenced by the alignment between training and testing contexts, as evidenced by systematic cross-context evaluations using an 80/20 train–test split per participant and quantitative metrics (accuracy, precision, recall, and F1-score) averaged across participants. The observed variations in performance across stress scenarios, classifiers, and EEG sensors provide empirical support for this claim.