Dr Roushanak Rahmat, PhD

AI Scientist

Freelancer, Speaker, Blogger, Researcher and Developer in Tech

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About Me

Roushanak is an accomplished AI scientist with a wealth of experience in computer vision, medical imaging, and data science. She holds a PhD in AI from Heriot-Watt University, earned through a collaboration with the prestigious University of Edinburgh. Roushanak's expertise spans a diverse range of areas, including computer vision, medical image analysis, image processing, signal processing, machine learning, deep learning, healthtech, and data science.

During her tenure at Elekta, a world-leading medical tech company, Roushanak made significant contributions to the field of AI in healthcare. She played a pivotal role in replacing traditional models with cutting-edge AI solutions and spearheaded groundbreaking projects aimed at improving radiotherapy treatment through the integration of advanced technologies. Her passion for incorporating uncertainty and diversity into structured AI models has enabled novel applications in healthcare. With a strong background in handling diverse medical and non-medical data, such as MRI, CT, CBCT, point cloud, and text, Roushanak utilizes Python and leading deep learning libraries like PyTorch, Keras, Jax, and TensorFlow to develop state-of-the-art AI solutions.

Beyond her professional work, Roushanak is an avid public speaker on technical AI topics and actively promotes diversity and inclusion as a Women Techmaker ambassador. She shares her knowledge and insights through her blog on Medium, content creation on Youtube, Instagram, and Twitter, as well as her open-source contributions on Github. As a freelancer, she embraces new challenges with enthusiasm, seeking opportunities to apply her expertise and make a positive impact. Roushanak's exceptional communication skills and experience in leading teams make her a valuable collaborator, fostering a collaborative environment that brings out the best in her colleagues.




Freelance, AI



AI Research Scientist, AI in Radiotherapy Imaging



Course Lecturer and Curriculum Developer, Artificial Intelligence

The Institute of Cancer Research,

University of London, UK

Deep Learning Research Fellow, Artificial Intelligence

Queens’ College,

University of Cambridge, UK

Postdoctoral Research Associate, Deep Learning in Medical Imaging

University of Cambridge

Cambridge Brain Tumour Imaging Laboratory, UK

Research Associate, Artificial Intelligence in Healthcare

Amirkabir University of Technology

Image Processing & Pattern Recognition Laboratory, Iran

Visiting Scholar, Artificial Intelligence in Image Analysis

University of St. Andrews,

School of Computer Science, UK

Research Fellow, Artificial Intelligence

Heriot-Watt Univerity, UK

Teaching Assistant

Courses: Signal Processing, Digital Image Processing, Circuit Analysis, C++

University Technology Petronas, Malaysia

Teaching Assistant

Courses: Numerical Methods, Signals and Systems, Image Processing

Alcatel-Lucent, Malaysia

Intern Engineer, Telecommunication


Heriot-Watt University in collaboration with The University of Edinburgh

2013 - 2017

PhD, Image Processing & Computer Vision

Institute of Sensors, Signals and Systems – Institute for Digital Communications – Cancer Research Center

Oxford Machine Learning Summer School


International Computer Vision Summer School


University Technology Petronas

2009 - 2012

MSc by Research, Image Processing

Department of Electrical & Electronics Engineering

University Technology Petronas

2005 - 2009

BEng(Hons), Electrical & Electronics Engineering (Instrumentation & Control)

Department of Electrical & Electronics Engineering


AI online course

Designed and created an online intermediate-level course covering the principles of AI. This consists of video lectures (in the topics of: CNN, RNN, NLP and GAN), python coding lectures, coding assignments and quizzes in each module.

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Deep learning for survival prediction

Built a novel deep learning architecture for early detection of cancer. Predicting site of lung cancer initiation and progression using merged image screening and patient’s demographic data in one network (NLST dataset).

Deep learning for vessel tree segmentation

Studied differet types of U-net model for the retina vessel segmentation application.

Automatic computational analysis of multi-modal brain tumour imaging

Developed and applied image segmentation models, in particular deep learning, for automatic segmentation of glioma and vestibular schwannoma before treatment and prediction of progression pattern after treatment, using conventional structural and diffusion tensor images

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New level set model in follow up radiotherapy image analysis

Developed new level set segmentation model with the least amount of dependency to its parameter settings

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Automatic segmentation of optic nerve using Archlight images

Automatic segmentation of the optic nerve’s (to calculate cup to disc ratio) for earlier detection of glaucoma.

Monitoring of lung cancer patients during radiotherapy using combined texture and level set analysis of CBCT Images

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3D shape reconstruction using shape from focus

Recovering the depth information of an object from a sequence of 2D images with varying focus is known as shape from focus.

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Online signature verification (by data glove) using SVD

The SVD technique is used to find r-singular vectors sensing the maximal energy of the signature data using data glove.

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1. Rahmat R, et al., Techniques for processing CBCT projections. US Patent App. 18/157,524 (2023).

2. Rahmat R, et al., Techniques for removing scatter from CBCT projections. US Patent App. 18/157,524 (2023).

3. Rahmat R, et al., Techniques for adaptive radiotherapy based on CBCT projection correction and reconstruction. US Patent App. 18/157,524 (2023).

4. Rahmat R, Harris-Birtill, D, Finn, D, Feng, Y, Montgomery, D, Nailon, W H, & McLaughlin, S. Radiomics-Led Monitoring of Non-small Cell Lung Cancer Patients During Radiotherapy. In 25th Annual Conference on Medical Image Understanding and Analysis 2021 (pp. 532-546). Springer.[Publisher's link] [Bibtex]

5. Wan Y, Rahmat R, Price SJ. Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival. Acta Neurochirurgica. 2020 Jul 13:1-4.[Publisher's link] [Bibtex]

6. Rahmat R, Saednia K, Khani MR, Rahmati M, Jena R, Price SJ. Multi-scale segmentation in GBM treatment using diffusion tensor imaging. Computers in Biology and Medicine. 2020 May 22:103815.[Publisher's link] [Bibtex]

7. Stefani A, Rahmat R, Harris-Birtill D. Autofocus Net: Auto-focused 3D CNN for Brain Tumour Segmentation. InAnnual Conference on Medical Image Understanding and Analysis 2020 Jul 15 (pp. 43-55). Springer, Cham. [Publisher's link] [Bibtex]

8. Rahmat R, Brochu F, Li C, Sinha R, Price SJ, Jena R. Semi-automated construction of patient individualised clinical target volumes for radiotherapy treatment of glioblastoma utilising diffusion tensor decomposition maps. The British Journal of Radiology. 2020 Apr;93(1108):20190441.[Publisher's link] [Bibtex]

9. Rahmat R, Harris-Birtill D. Comparison of level set models in image segmentation. IET Image Processing. 2018 Aug 28;12(12):2212-21.[Publisher's link] [Bibtex]

10. Rahmat R, Nailon WH, Price A, Harris-Birtill D, McLaughlin S. New level set model in follow up radiotherapy image analysis. InAnnual Conference on Medical Image Understanding and Analysis 2017 Jul 11 (pp. 273-284). Springer, Cham.[Publisher's link] [Bibtex]

11. Rahmat R. Monitoring of lung cancer patients during radiotherapy using combined texture and level set analysis of CBCT images (Doctoral dissertation, Heriot-Watt University)[Publisher's link] [Bibtex]

12. Rahmat R, Yang F, William WH, McLaughlin S. Lung Tumour Segmentation using a Combined Texture and Level Set [Publisher's link]

13. Rahmat R, Malik AS, Kamel N, Nisar H. 3D shape from focus using LULU operators and discrete pulse transform in the presence of noise. Journal of visual communication and image representation. 2013 Apr 1;24(3):303-17.[Publisher's link] [Bibtex]

14. Rahmat R, Mallik AS, Kamel N, Choi TS, Hayes MH. 3D shape from focus using LULU operators. InInternational Conference on Advanced Concepts for Intelligent Vision Systems 2012 Sep 4 (pp. 237-245). Springer, Berlin, Heidelberg.[Publisher's link] [Bibtex]

15. Rahmat R, Malik AS, Faye I, Kamel N, Nisar H. An overview of LULU operators and discrete pulse transform for image analysis. The Imaging Science Journal. 2013 Feb 1;61(2):146-59.[Publisher's link] [Bibtex]

16. Rahmat R. Shape from Focus Using Lulu Operators and Discrete Pulse Transform in the Presence of Noise. [Publisher's link]

17. Rahmat R, Malik AS, Kamel N. 3-D content generation using optical passive reflective techniques. In2011 IEEE 15th International Symposium on Consumer Electronics (ISCE) 2011 Jun 14 (pp. 639-642). IEEE. [Publisher's link] [Bibtex]

18. Rahmat R, Malik AS, Kamel N. Comparison of LULU and median filter for image denoising. International Journal of Computer and Electrical Engineering. 2013 Dec 1;5(6):568.[Publisher's link] [Bibtex]

19. Rahmat R, Kamel NS, Yahya N. Principle Subspace-Based Signature Verification Technique using Reduced Sensors Data Glove. In2009 Innovative Technologies in Intelligent Systems and Industrial Applications 2009 Jul 25 (pp. 317-321). IEEE.[Publisher's link] [Bibtex]

20. Rahmat R, Kamel NS, Yahya N. Subspace-based signature verification technique using reduced-sensor data glove. In2009 IEEE Symposium on Industrial Electronics & Applications 2009 Oct 4 (Vol. 1, pp. 83-88). IEEE.[Publisher's link] [Bibtex]

21. Rahmat R, Online Signature Verification using SVD Method.[Publisher's link]

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