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Md Abulkalam Azad

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Md Abulkalam Azad

PhD Candidate, AI for Ultrasound Medical Imaging
Department of Circulation and Medical Imaging
Faculty of Medicine and Health Sciences

md.a.azad@ntnu.no
+4797336992 343.03.K03A 0340 AHL Øya, Trondheim
ResearchGate Google Scholar
About Research Publications Outreach

About

I am pursuing a PhD in ultrasound medical imaging, focusing on leveraging computer vision and deep learning techniques.

Before joining ISB, I completed my master's degree in Marine and Maritime Intelligent Robotics at the Department of Marine Technology, NTNU. This was an Erasmus Mundus Joint Master's Degree program jointly offered by Université de Toulon (UTLN) and NTNU. Following my first year at UTLN, I had the opportunity to work as a summer research student within the computer vision group at SINTEF Digital. During this time, my focus was on the 3D reconstruction of challenging objects using the Neural Radiance Field (NeRF) technique. Consequently, my master's thesis was also carried out within this group, where I delved into Multi-label Video Classification for Underwater Ship Inspection. My research specifically revolved around the investigation and analysis of multi-attention-based transformer and vision transformer models within the context of underwater environments for video understanding.

I worked as a Software Engineer at SAMSUNG R&D in Bangladesh for one and a half years before starting my master's study. During this period, I earned valuable skills in different Samsung Software Development Kits (SDKs) such as AR Emoji, Galaxy Watch Face, Samsung IAP, and Samsung DeX. 

In 2019, I completed my bachelor's degree in Computer Science and Engineering at United International University in Bangladesh. During my undergraduate program, I had the privilege of receiving the Erasmus Mundus Scholarship, which allowed me to pursue a 10-month academic stint at Universität Bremen in Germany. As part of this experience, I undertook my thesis project, which concentrated on the Enhencement of a Logistic Simulation Scenario using 3D Computer Graphics.

Competencies

  • Computer Vision
  • Deep Learning
  • Motion Estimation
  • NeRF
  • Point Tracking
  • Vision Transformer

Research

PhD topic: Advancing Myocardial Function Imaging in Echocardiography using Vision Intelligence

  • Task 01: Develop and apply a novel deep learning-based point tracking method for motion estimation in echocardiographic data.
  • Task 02: Extend the automated strain estimation pipeline to support multiple cardiac views and regional myocardial strain analysis.
  • Task 03: Explore an AI-assisted, human-in-the-loop system to enhance strain measurement accuracy and support efficient data annotation.
  • Task 04: Validate the research-driven, fully automated strain estimation pipeline across relevant patient cohorts to assess clinical applicability.
  • Ultrasound Reaseach Group

EchoTracker: Advancing Myocardial Point Tracking in Echocardiography (MICCAI 2024)

We present EchoTracker, a two-stage model for tissue tracking in echocardiography. It achieves 67% accuracy, 2.86-pixel median error, and improves GLS by 25% over state-of-the-art methods.

Publications

  • Chronological
  • By category
  • All publications registered in NVA

2024

  • Azad, Md Abulkalam; Chernyshov, Artem; Nyberg, John Anders Tomas; Tveten, Ingrid Elisabeth; Løvstakken, Lasse; Dalen, Håvard. (2024) EchoTracker: Advancing Myocardial Point Tracking in Echocardiography.
    Academic chapter/article/Conference paper

2023

  • Azad, Md Abulkalam; Mohammed, Ahmed Kedir; Waszak, Maryna; Elvesæter, Brian; Ludvigsen, Martin. (2023) Multi-label Video Classification for Underwater Ship Inspection.
    Academic chapter/article/Conference paper

2021

  • Azad, Md Abulkalam; Islam, Sadia; Farid, Dewan Md.; Shatabda, Swakkhar. (2021) Layered Ensemble Learning for Effective Binary Classification.
    Academic chapter/article/Conference paper

2019

  • Sabah, Shabnam; Anwar, Sara Zumerrah Binte; Afroze, Sadia; Azad, Md Abulkalam; Shatabda, Swakkhar; Farid, Dewan Md.. (2019) Big Data with Decision Tree Induction.
    Academic chapter/article/Conference paper

Part of book/report

  • Azad, Md Abulkalam; Chernyshov, Artem; Nyberg, John Anders Tomas; Tveten, Ingrid Elisabeth; Løvstakken, Lasse; Dalen, Håvard. (2024) EchoTracker: Advancing Myocardial Point Tracking in Echocardiography.
    Academic chapter/article/Conference paper
  • Azad, Md Abulkalam; Mohammed, Ahmed Kedir; Waszak, Maryna; Elvesæter, Brian; Ludvigsen, Martin. (2023) Multi-label Video Classification for Underwater Ship Inspection.
    Academic chapter/article/Conference paper
  • Azad, Md Abulkalam; Islam, Sadia; Farid, Dewan Md.; Shatabda, Swakkhar. (2021) Layered Ensemble Learning for Effective Binary Classification.
    Academic chapter/article/Conference paper
  • Sabah, Shabnam; Anwar, Sara Zumerrah Binte; Afroze, Sadia; Azad, Md Abulkalam; Shatabda, Swakkhar; Farid, Dewan Md.. (2019) Big Data with Decision Tree Induction.
    Academic chapter/article/Conference paper

Outreach

2025

  • Academic lecture
    Azad, Md Abulkalam; Østvik, Andreas. (2025) Speckle Tracking Echocardiography using Point Tracking: A Paradigm Shift?. Meeting on Myocardial Function Imaging 2025 , KU Leuven, Belgium 2025-02-06 - 2025-02-07

2023

  • Lecture
    Azad, Md Abulkalam; Mohammed, Ahmed Kedir; Waszak, Maryna; Elvesæter, Brian; Ludvigsen, Martin. (2023) MViST: A Multi-label Vision Spatiotemporal Transformer for Underwater Ship Inspection. NORA Annual Conference 2023 , Tromsø 2023-06-05 - 2023-06-06
  • Academic lecture
    Azad, Md Abulkalam; Mohammed, Ahmed Kedir; Waszak, Maryna; Elvesæter, Brian; Ludvigsen, Martin. (2023) Multi-label Video Classification for Underwater Ship Inspection. OCEANS Conference & Exposition , Limerick, Ireland 2023-06-05 - 2023-06-08
  • Lecture
    Azad, Md Abulkalam. (2023) Underwater Robotics and Erasmus Mundus Scholarship. From Scholar to Explorer: A Tale of Underwater Robotics and Erasmus Mundus Scholarship , Dhaka, Bangladesh 2023-08-02 - 2023-08-02

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