Nouman Ahmad
Doktorand vid Institutionen för kirurgiska vetenskaper; Radiologi; Radiologisk bildanalys
- E-post:
- nouman.ahmad@uu.se
- Besöksadress:
- Dag Hammarskjölds v 14 B Floor 2
75237 Uppsala - Postadress:
- Dag Hammarskjölds v 14 B Floor 2
75237 Uppsala
- CV:
- Ladda ned CV
- ORCID:
- 0000-0003-0202-9205
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Nyckelord
- artifical intelligence
- computer science
- computer vision
- convolutional neural networks
- data science
- deep learning
- image processing
- machine learning
- medical image analysis
Biografi
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Nouman Ahmad is Working as Doctoral Student at Uppsala University. He also worked as a Research Assistant in, the Department of Surgical Sciences, Radiology; Radiological image analysis, at Uppsala University. He completed his MS degree in Computer Science from COMSATS University Islamabad (CUI), Islamabad Campus, Islamabad, Pakistan in 2020. He also worked as a Research Assistant at the COMSATS University Islamabad, Pakistan in the field of medical imaging, data science, computer vision, and machine learning. Currently, He is working on solving several computer vision problems such as image classification, segmentation, object detection, and image super-resolution by applying several approaches such as supervised learning, semi-supervised, self-supervised learning, generative adversarial networks (GAN), and clustering. His research interests include data science, medical imaging, computer vision, image processing, deep learning, and explainable AI.
Publikationer
Urval av publikationer
Senaste publikationer
- Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol (2024)
- Improved automated tumor segmentation in whole-body 3D scans using multi-directional 2D projection-based priors (2024)
- Automatic segmentation of large-scale CT image datasets for detailed body composition analysis. (2023)
Alla publikationer
Artiklar
- Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol (2024)
- Improved automated tumor segmentation in whole-body 3D scans using multi-directional 2D projection-based priors (2024)
- Automatic segmentation of large-scale CT image datasets for detailed body composition analysis. (2023)