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Hassan Ahmed Sial is an experienced researcher passionate about applying his expertise in computer vision and machine learning to develop solutions that can improve patient lives and positively impact society. He has worked on several projects throughout his career in the fields of medical imaging, computer vision, machine learning, and deep learning, with a particular interest in combining computer vision and deep learning for various applications in biomedical imaging.

Hassan completed his Ph.D. in 2021 with excellent Cum Laude in Computer Vision Center from the Universitat Autònoma de Barcelona, where he focused on ground truth generation, intrinsic image decomposition, and estimating lighting effects from single images using deep learning-based techniques. His research work during his Ph.D. has been published in several high-impact factor journals and conference proceedings.

Currently, Hassan is a Postdoctoral Researcher at the Barcelona Institute for Global Health (ISGlobal) and a member of the Biomedical Data Science Research Group. His current research work involves developing novel solutions using computer vision, machine learning, and deep learning to understand disease drivers and develop diagnostic computational methods. He is currently working on a groundbreaking project in collaboration with the startup company called Newborn Solution (NBS). The project is focused on leveraging ultrasound images to diagnose infant meningitis at an early stage, providing a non-invasive, cheaper, and child-friendly solution.

Prior to his current position, Hassan worked as a Postdoctoral Researcher at the University of Girona in Spain, where he was involved in the EU H2020 project iToBoS (“Intelligent Total Body Scanner for Early Detection of Melanoma”) for early detection of skin cancer using advanced deep learning methods. In this project, he contributed to the development of novel ideas for image classification and interpretation and participated in experiments for the integration of software for skin cancer detection.

Lines of research

  • Biomedical Data Science and Artificial Intelligence
  • Medical imaging
  • Image processing/computer vision
  • Deep learning-based solutions for early diagnosis in healthcare

Main publications

  • Hassan A. Sial “Estimating Light Effects from a Single Image: Deep Architectures and Ground-Truth Generation” Ph.D. Thesis Universitat Autònoma de Barcelona (2021)
  • Sagnik Das, Hassan A. Sial, Ke Ma, Ramon Baldrich, Maria Vanrell and "Dimitris Samaras “Intrinsic Decomposition of DocumentImages In-the-Wild" BMVC (2020)
  • Hassan A. Sial, Ramon Baldrich, and Maria Vanrell "Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects," Journal of the Optical Society of America A (2020)
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