Sulaiman Vesal

Currently, I am an AI researcher at Hanwha Vision America working on computer vision problems including object detection, action recognition and multi-modal imaging. Previously, I worked as a R&D Scientist and Engineer at Urologic Cancer Innovation Lab (UCIL) in Stanford. I am also a PhD candidate, supervised by Prof. Dr.-Ing. habil. Andreas Maier at the Pattern Recognition Lab, in FAU Erlangen-Nuremberg.

I have done my M.Sc. in computer science at department of computer science in South Asian University in 2013, Delhi, India. Before that, I have received my B.Sc. degree in computer science also from Kabul University in 2010.

Email: sulaiman.vesal@fau.de

Email / Google Scholar / Twitter / Github / ResearchGate

Research

My research interests lie in medical image processing, computer vision, deep learning and machine learning. I had a great time developing deep learning models for cardiovascular MR segmentation/quantification, usnupervised domain adaptation for corss-modality imaging and multi-modal breast cancer diagonsis.

News

[Dec 2023] Our ProsDectNet: Bridging the gap for prostate cancer detection on Transrectal Ultrasound paper was accepted to Medical Imaging workshop at NeurIPS 2023.

[Feb 2022] Our Domain Generalized Prostate Gland Segmentation on Transrectal Ultrasound Images paper was accepted to Medical Image Analysis (MedIA). (Impact Factor: 10.8)

[Mar 2021] Our Adapt Everywhere UDA paper was accepted to IEEE Transaction in Medical Imaging (IEEE-TMI). (Impact Factor: 6.8)

[Jan 2021] Our Spatio-Temporal Multi-task Learning for Full LV Quantification was accepted to IEEE Journal of Biomedical Health and Informatic (IEEE-TMI). (Impact Factor: 5.2)

[Feb 2021] Started my new job as a R&D sceintist and Engineer at Urologic Cancer Innovation Lab (UCIL) in Stanford.

[Aug 2020] Started my job as a research data scientist at ArnaoutLab at University of California San Francisco (UCSF).

[July 2020] I have finished my PhD (Defense is pending -_-) at at the Pattern Recognition Lab, in FAU Erlangen-Nuremberg, Germany.

[Apr 2020] Our journal paper titled Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks was accepted to MDPI Jounral of Imaging. (Impact Factor: 2.0)

[Oct 2019] We gave a talk about our supervised UDA method for mulit-sequence myocaridal segmentation in MICCAI-STCOM 2019.

[Oct 2019] We won the second place for Multi-sqeuence Cardiac-MRI segmentation challenge at MICCAI-STCOM 2019.

[Oct 2018] We won the second place for Atrial Segmentation Challenge at MICCAI-STCOM 2018.

Publications

	Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac-MR Segmentation Sulaiman Vesal, Mingxuan Gu, Ronak Kosti, Andreas Maier, Nishant Ravikumar IEEE Transaction in Medical Imaging, 2021 project page / arXiv / IEEE-TMI / Code
	Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification Sulaiman Vesal, Mingxuan Gu, Andreas Maier, Nishant Ravikumar IEEE Journal of Biomedical Health and Informatic (IEEE-JBHI), 2020 project page / arXiv / IEEE-JBHI / code /
	Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks Sulaiman Vesal, Andreas Maier, Nishant Ravikumar MDPI Journal of Imaging, 2020 project page / arXiv / Journal of Imaging / code /
	Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation Sulaiman Vesal, Nishant Ravikumar, Andreas Maier MICCAI-STACOM, 2019 arXiv / paper

Journal Reviewers

IEEE Transactions on Medical Imaging

Medical Image Analysis

IEEE Transcation on Image Processing

Nature Scientific Report

PLoS One

International Journal of Computer Assisted Radiology and Surgery

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