Riad Hassan

I am Riad Hassan, a passionate researcher and lecturer specializing in computer vision, medical image analysis, and deep learning. I hold an MSc from BUET (GPA 3.92/4.00, top 1%), where I developed an uncertainty-driven boundary-refined CNN for medical image segmentation.

With a proven record of publications in venues such as IEEE ISBI and Biomedical Signal Processing and Control, my research focuses on efficient deep learning network design, attention-based architectures, and adaptive loss functions. As part of the BioRAIN Research Group; Data Analytics Lab, IICT, BUET; and Computer Vision Research Cell, CSE, GUB, I contribute to cutting-edge projects in AI, Computer Vision, and Medical Imaging.

I am passionate about conducting impactful research and contributing to leading journals (TMI, PAMI, MIA, TIP) and conferences (CVPR, MICCAI, MIDL, ISBI, ICLR, ECCV). With expertise in Python, PyTorch, and large-scale experimentation, my goal is to advance the state of the art in Computer Vision, Medical Imaging and AI.

News

Oct 22, 2025	Guiding Undergraduate Researchers – A Seminar at Pabna University of Science and Technology
Sep 13, 2025	Our paper EDLDNet: An efficient dual-line decoder with multi-scale convolutional attention for multi-organ segmentation has been published in Biomedical Signal Processing and Control (Q1, IF: 4.9).
May 23, 2025	Our paper MobDenseNet: Brain tumor classification from MRI has been published in Array (Q1, IF: 4.5).
Dec 15, 2024	Our paper on Object Detection in adverse weather has been presented at STI 2024.
Nov 30, 2024	Successfully completed MSc from BUET! Thesis: Uncertainty driven boundary refined CNN for medical image segmentation.

Latest Posts

May 22, 2025	Box Plot vs Violin Plot: Which One is More Powerful for Research Articles?

Selected Publications

BPSC

An efficient dual-line decoder network with multi-scale convolutional attention for multi-organ segmentation

Riad Hassan, M. Rubaiyat Hossain Mondal, Sheikh Iqbal Ahamed, Fahad Mostafa, and Md Mostafijur Rahman

Biomedical Signal Processing and Control, 2026

DOI Bib PDF Code

@article{HASSAN2026108611,
  title = {An efficient dual-line decoder network with multi-scale convolutional attention for multi-organ segmentation},
  journal = {Biomedical Signal Processing and Control},
  volume = {112},
  pages = {108611},
  year = {2026},
  issn = {1746-8094},
  doi = {https://doi.org/10.1016/j.bspc.2025.108611},
  url = {https://www.sciencedirect.com/science/article/pii/S174680942501122X},
  author = {Hassan, Riad and Mondal, M. Rubaiyat Hossain and Ahamed, Sheikh Iqbal and Mostafa, Fahad and Rahman, Md Mostafijur},
  keywords = {Attention mechanisms, Computational efficiency, Deep learning, Dual-line decoder, Medical image segmentation, Multi-scale feature extraction},
}

UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation

Riad Hassan, M. Rubaiyat Hossain Mondal, and Sheikh Iqbal Ahamed

PLOS ONE, Jun 2024

DOI Bib Code

@article{10.1371/journal.pone.0304771,
  doi = {10.1371/journal.pone.0304771},
  author = {Hassan, Riad and Mondal, M. Rubaiyat Hossain and Ahamed, Sheikh Iqbal},
  journal = {PLOS ONE},
  publisher = {Public Library of Science},
  title = {UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation},
  year = {2024},
  month = jun,
  volume = {19},
  pages = {1-18},
  number = {6},
}

ISBI

Uncertainty Driven Bottleneck Attention U-Net For Organ at Risk Segmentation

Abdullah Nazib, Riad Hassan, Zahidul Islam, and Clinton Fookes

In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Jun 2024

DOI Bib PDF Code

@inproceedings{10635587,
  author = {Nazib, Abdullah and Hassan, Riad and Islam, Zahidul and Fookes, Clinton},
  booktitle = {2024 IEEE International Symposium on Biomedical Imaging (ISBI)},
  title = {Uncertainty Driven Bottleneck Attention U-Net For Organ at Risk Segmentation},
  year = {2024},
  volume = {},
  number = {},
  pages = {1-5},
  address = {Athens, Greece},
  doi = {10.1109/ISBI56570.2024.10635587},
}