Prof. Essam Rashed
Principal Investigator
AI • Medical Imaging • Multimodal Systems
Building trustworthy AI for medical imaging and clinical decision support.
We develop segmentation, detection, report-generation, and agentic AI systems that integrate imaging, clinical text, and external knowledge - emphasizing robustness, uncertainty, explainability, and real-world deployment.
Agentic AI
Multimodal LLM/VLM
Medical Image Segmentation
Federated Learning
Uncertainty & Safety
Regulatory-ready Evaluation
News & Highlights
E. Rashed & M. Mabork are Co-Chairs of the 2026 IEEE International Conference on Future Machine Learning and Data Science
@ Kobe, Japan, 20-23 Nov. 2026
A paper is accepted for the 23rd IEEE International Conference on Learning and Technology (L&S26) @ KSA
Twin Minds in Cyber-Defense: A Dual-Agent Framework for Safe Automated Assessment in Security Education
E. Rashed and Y. Jia (M2) participated in the JST Nexus project workshop
@ Nanyang Technological University, Singapore, 28-29 Jan. 2026
E. Rashed and Y. Jia (M2) participated in the AAAI 2026 Conference
@ Singapore, 20-27 Jan. 2026
E. Rashed, A. Elboardy (D1) and M. Yousef (RS) participated in the IFMIA 2026
@ Kaohsiung, Taiwan, 12-14 Jan. 2026
A paper is published in Biomedical Signal Processing and Control (IF24=4.9)
Synthetic histopathology with controllable class distribution: A dual-GAN framework for melanoma segmentation
All lab members will participate in Joint Seminar with the TDSAI Lab @ Institute of Science Tokyo
@ Tokyo, 4-5 Feb. 2026
E. Rashed is Keynote Speaker at the Second Subtle Visual Computing Workshop @ CVPR
@ Denver, Colorado, USA, 3-7 June 2026
Research Areas
Segmentation & Detection
nnU-Net style pipelines, foundation-model adaptation, and clinically meaningful evaluation protocols.
Multimodal LLM/VLM
Radiology report generation, VQA, and structured reasoning over images + text with retrieval.
Federated & Privacy-Preserving AI
Cross-institution learning with domain shift handling, robust aggregation, and governance-aware workflows.
Uncertainty & Safety
Calibration, abstention, and uncertainty-aware decision support for high-stakes deployment.
Agentic AI Systems
Multi-agent orchestration for iterative analysis, conflict resolution, and transparent synthesis.
Regulatory-Ready Evaluation
Quality management, documentation, and reproducible benchmarks aligned with clinical practice.
People
Prof. Ghada Khoriba
Visiting Professor
Prof. Mohamed Mabrok
Visiting Professor
Ahmed Elboardy
PhD Student (D2)
Yiming Jia
PhD Student (D1)
Mina Yousef
PhD Student (D1)
Makoto Kanehira
MSc Student (M2)
Joining soon...
Research Student
Alumni
[2025] Hiroyuki Seshimo
Selected Publications
📝 Advances in Medical Image Segmentation: A Comprehensive Review with a Focus on Lumbar Spine Applications (2025)
Selected Awards
🏆 MEXT Science and Technology Award (Development Category)
🏆 Best Poster Award
Join the Lab
We’re looking for
- We are seeking highly motivated and intellectually curious students with a strong foundation in mathematics, programming (e.g., Python), and machine learning, who are passionate about advancing AI-driven medical imaging research and are eager to engage in rigorous, collaborative, and publication-oriented research within an international environment.
How to apply
- Email your CV + short research statement
- Include links to papers / code (if available)
- Write 2–3 topics you want to work on
Tip: If you’re applying for scholarships (JSPS/MEXT/etc.), mention the program and deadline.
Contact
Lab Information
Affiliation: Graduate School of Information Science, University of Hyogo
Address: 7-1-28 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
Room: Lab. number 601 (6th floor)