Dr Faraz Janan

Faraz Janan For media enquiries please contact the Press Team
Senior Lecturer
Faculty:
Faculty of Science and Engineering
School:
Computing and Information Science
Location:
Cambridge
Areas of Expertise:
Artificial Intelligence , Computer Science
Research Supervision:
Yes

Faraz is a Senior Lecturer in artificial intelligence and computing at ARU in Cambridge. His research interests include medical image analysis and augmented reality in medicine. He is a member of the Computing, Informatics and Applications Research Group.

[email protected]

Background

Before joining ARU, Faraz worked as a senior lecturer in computer vision at the University of Lincoln for over five years. He has conducted supervision of several PhD students in the field of AI applied to medical image analysis.

Dr Janan is a Fellow of the Higher Education Academy (HEA) UK and a qualified mental health first aider. He has served as the Director and Governor of Lincoln University Technical College (UTC). He is also Associate Editor for the Nature British Journal of Cancer, and Department Senior Tutor at the Department of Bioengineering, Imperial College London.

Prior to joining Lincoln Technical College, Faraz worked as a medical imaging scientist at Volpara Health Ltd., a cutting-edge AI company in New Zealand.

Faraz takes pride in being the PhD student (and subsequently a mentee in industry) of the world-renowned British scientist Professor Sir Michael Brady FRS FREng FMedSci at Oxford.

Spoken Languages
  • Urdu
  • Hindi
  • Pashto
  • English
Research interests
  • Medical image analysis
  • Augmented reality in medicine
  • Artificial intelligence and machine learning
Areas of research supervision
  • Medical image analysis
  • Augmented reality in medicine
  • Artificial intelligence and machine learning
Teaching

Modules:

  • AI Techniques
  • Deep Learning
  • Image Processing
  • Mathematics and Statistics
  • Principles of Artificial Intelligence
  • Principles of Data Mining and Machine Learning
  • Principles of Data Science
  • Postgraduate Major Project
  • Final Project
Qualifications
  • Post Doctorate, Department of Genetics, University of Cambridge
  • DPhil in Engineering, University of Oxford
  • MEng in Information Engineering, University of Liverpool
  • BS in Computer Engineering, CUST, Pakistan
Memberships, editorial boards
  • Associate Editor, Nature British Journal of Cancer
  • Fellow, Higher Education Academy UK
Research grants, consultancy, knowledge exchange
  • Daniel Turnberg Fellowship: £4,000 (2021)
  • EPSRC doctoral studentship (up to £90,000; 2021)
  • Daniel Turnberg Fellowship: £3,000 (2019)
  • College of Science equipment award: £3,500 (2018)
  • Lincoln Academy of Learning and Teaching: £1,000 (2018)
  • IDB Merit Scholarship Award: up to £200,000 (2011)
  • HEC Scholarship Award: up to £25,000 (2006)
Selected recent publications

Amer, Alyaa, Xujiong Ye, and Faraz Janan, 2021. ResDUnet: A Deep Learning based Left Ventricle Segmentation Method for Echocardiography. IEEE Access.

Amer, Alyaa, Xujiong Ye, and Faraz Janan, 2021. Residual Dilated U-Net for the Segmentation of COVID-19 Infection From CT Images. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.462-470.

Janan, Faraz, and Michael Brady. "RICE: A method for quantitative mammographic image enhancement." Medical image analysis 71 (2021): 102043.

Allison, Benjamin, Xujiong Ye, and Faraz Janan, 2020. Breast3D: An Augmented Reality System for Breast CT and MRI. In 2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), pp.247-251.

Allison, Benjamin, Xujiong Ye, and Faraz Janan, 2020. MIXR: A Standard Architecture for Medical Image Analysis in Augmented and Mixed Reality. In 2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), pp.252-257.

Janan, Faraz, and Tariq Asma. Personal Tutorage System for Schools in Pakistan: A Policy Proposal. SocArXiv.

Amer, Alyaa, Xujiong Ye, Massoud Zolgharni, and Faraz Janan, 2020. ResDUnet: Residual Dilated UNet for Left Ventricle Segmentation from Echocardiographic Images. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp.2019-2022.

Ghatwary, Noha, Massoud Zolgharni, Faraz Janan, and Xujiong Ye, 2020. Learning spatiotemporal features for esophageal abnormality detection from endoscopic videos. IEEE Journal of Biomedical and Health Informatics, 25(1), pp.131-142.

Azarmehr, Neda, Xujiong Ye, Faraz Janan, James P. Howard, Darrel P. Francis, and Massoud Zolgharni, 2020. Automated Segmentation of Left Ventricle in 2D echocardiography using deep learning. arXiv preprint arXiv:2003.07628.