MD MEHEDI·HASSAN
SLICE 08 · News & Media Coverage

Press & Updates

Announcement · 2026

Named in the world’s top 2% of scientists

Listed in the 2026 Top 2% Scientists ranking compiled by Stanford University and published by Elsevier — the single-year citation-impact list that covers roughly the top two per cent of researchers worldwide across every scientific field.

The listing places the work in Artificial Intelligence & Image Processing, under the broader field of Information & Communication Technologies.

Listed year2026 · single-year data
Main fieldInformation & Communication Technologies
Sub-fieldArtificial Intelligence & Image Processing
Global rank184,513
Rank in sub-field8,326
Scopus h-index16 · hm-index 4

The h-index shown on the certificate is Scopus-based, which is how the Stanford & Elsevier list is computed. The Google Scholar h-index of 27 quoted elsewhere on this site counts a wider set of sources, so the two numbers are measuring different things.

Top 2% Scientists 2026 certificate for Md. Mehedi Hassan, verified from Elsevier and Stanford University data

2026

Conference

IEEE HealthCom 2026, New York

Presented my first Ph.D. research work — Tri-Planar Fourier Context Learning for Lightweight 3D Liver Segmentation — at the IEEE International Conference on E-health Networking, Application & Services, held 19–21 October 2026 in New York City. The paper is an industry-linked collaboration with Stanford University and Emory University, and the method targets accurate 3D liver segmentation at a fraction of the usual computational cost. With thanks to my supervisor for the guidance and the faith in the work.

Md. Mehedi Hassan with his supervisor beside the VesselMambaLite poster at IEEE HealthCom 2026 in New York
With my supervisor at the poster session — IEEE HealthCom 2026, New York City
Conference site →
Recognition

Top 2% Scientists 2026 — Stanford University & Elsevier

Included in the single-year citation-impact ranking for Artificial Intelligence & Image Processing. See the announcement above.

About the list →
In the media · August 2026

DermaViGNet — explainable AI for skin disease diagnosis

A hybrid Vision Transformer and CNN framework for skin disease classification, built with explainable AI so clinicians can see which regions of an image drove a diagnosis. Trained and validated on nearly 10,000 dermoscopic images spanning common and underrepresented conditions, it reached 98% accuracy across five dermatological conditions — vitiligo, acne, nail psoriasis, hyperpigmentation, and the rare, life-threatening Stevens-Johnson Syndrome–Toxic Epidermal Necrolysis — outperforming eight baseline deep learning models. The work is an Australia–Bangladesh research collaboration.

98%Classification accuracy
5Dermatological conditions
~10,000Dermoscopic images
8Baseline models outperformed

2025

Keynote

DICTA 2025, Adelaide

Keynote on evolutionary neural architecture search for brain tumor segmentation networks.

See conference leadership →

2024

Feature

Springer Nature Editorial Coverage

Official feature announcement recognizing the Best Paper Award for research in AI healthcare analytics.

Read the feature →
Thought Leadership

How Healthcare Informatics Will Shape the Future of Healthcare Delivery

Commentary for Health Data Management on clinical AI workflows and data infrastructure.

Read the article →
Thought Leadership

How Data Interoperability Can Transform Patient Care

Expert commentary for Health Data Management on health data exchange, interoperability standards, and clinical outcomes.

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Fellowship

National ICT Research Fellowship

Conferred by the ICT Division, Government of Bangladesh, for contributions to biomedical AI and EEG signal processing.

See experience →