IEEE EMB and ACM Baltimore Meeting: a Seminar on Deep Learning Algorithms for Chest Radiography Analysis

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The IEEE EMB Society Baltimore Chapter and ACM Baltimore Chapter cordially invite you to attend our upcoming seminar!  

Chest radiography image analysis for thoracic (chest region) disease detection has been an active area of research in the last decade. Our distinguished guest speaker, Dr. Taufiq Hasan, Bangladesh University of Engineering and Technology (BUET), will delve into the challenges of domain generalization, anatomy awareness, and image superposition in chest X-ray imaging. He will discuss the recent advances in deep learning models in this domain and share his team's recent research findings that address these specific challenges.

Furthermore, the seminar will provide you with an opportunity to network, meet, and interact with the speaker and other attendees to expand your knowledge and network. The meeting will be held at Johns Hopkins University Applied Physics Laboratory (JHU/APL) Building 201 (201-117), located at 11091 Johns Hopkins Road, Laurel, MD 20723. What you need to know before coming to APL, Visitors Information 


  Date and Time




  • Date: 17 Apr 2024
  • Time: 05:15 PM to 07:30 PM
  • All times are (UTC-04:00) Eastern Time (US & Canada)
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  • Johns Hopkins University Applied Physics Laboratory (JHU/APL)
  • 11091 Johns Hopkins Road
  • Laurel, Maryland
  • United States 20723
  • Building: JHU/APL, 201-117
  • Click here for Map

  • Contact Event Hosts
  • ACM Baltimore Chapter - Ashutosh Dutta

    IEEE EMB Baltimore Chapter -  Carole Carey, Walter Galvez




  • Co-sponsored by IEEE Engineering in Medicine and Biology (EMB) Society & Association for Computing Machinery (ACM) Baltimore Chapter
  • Starts 07 April 2024 12:00 AM
  • Ends 16 April 2024 11:59 PM
  • All times are (UTC-04:00) Eastern Time (US & Canada)
  • No Admission Charge


Dr. Taufiq Hasan of Bangladesh University of Engineering and Technology (BUET)


Advancements in Chest X-ray Analysis: Deep Learning Models Addressing Domain Generalization and Anatomy-aware Learning

Chest radiography is one of the most frequently performed medical imaging examinations globally. However, there is a significant shortage of expert radiologists, especially in low- and middle-income countries (LMICs). Artificial Intelligence (AI) based automatic analysis of X-ray images and medical imaging modalities has thus been a critical area of research and innovation in recent times. However, traditional deep learning-based X-ray image analysis is faced with several challenges. This talk highlights the following three key challenges and discusses our novel solutions. (i) Deep learning model performance degrades due to the variability of X-ray images collected from different source domains. (ii) Traditional models do not have explicit knowledge about human anatomical structures within the images, which is vital for accurate disease diagnosis. (iii) Radiographic projection creates superimposed images with overlapping anatomical structures that are difficult to disentangle. The talk will present our recent research findings and algorithmic advances addressing these three challenges. Proposed algorithms are evaluated and compared to state-of-the-art methods. Experimental evaluations were conducted using well-known benchmark data sources, including the NIH, MIMIC, and Stanford CheXpert. The talk will conclude with a discussion on possible future directions in this area to further improve the automatic analysis of X-ray images.



Dr. Taufiq Hasan (Senior Member, IEEE) completed his BSc. and MSc. in Electrical and Electronic Engineering (EEE) from Bangladesh University of Engineering and Technology (BUET) and his Ph.D. in Electrical Engineering from the University of Texas at Dallas (UTD). He worked as a Research Scientist at the Robert Bosch Research and Technology Center, Palo Alto, CA. Dr. Hasan is currently an Associate Professor of the Department of Biomedical Engineering at BUET, where he leads the mHealth research group. He co-invented the OxyJet CPAP, a low-cost, non-invasive ventilator device for hypoxemic COVID-19 patients, which was the first medical device designed, developed, and regulatory approved in Bangladesh. He holds 20+ peer-reviewed research papers and several US patents/patent applications. His research interests include biomedical signal and medical image analysis using deep learning, and innovative medical device design for low-resource healthcare settings.

Dr. Hasan is also an adjunct faculty member at the Center for Bioengineering Innovation and Design (CBID), Johns Hopkins University, Baltimore, MD.


Address:Bangladesh University of Engineering and Technology, Dhaka, BD, Bangladesh


In-Person Attendance Only! 

5:15 pm -  Registration, Refreshments, Networking

5:45 pm - Welcome and Introduction

6:00 pm - Presentation, Dr. Taufiq Hasan "Advancements in Chest Radiograph Image Analysis: Deep Learning Models Addressing Domain Generalization, Anatomy-awareness, and Image Superimposition"

7:00 pm - Group Picture, Networking

7:30 pm - Adjourn

Joint meeting. IEEE Engineering in Medicine and Biology (EMB) Baltimore Chapter & Association of Computing Machinery (ACM) Baltimore Chapter