Dr Ali Gooya

  • Senior Lecturer in Machine Learning (School of Computing Science)

telephone: 0141 330 1637
email: Ali.Gooya@glasgow.ac.uk

Glasgow, Glasgow City, Scotland, United Kingdom, Sir Alwyn William Building, G12 8QN

Import to contacts

ORCID iDhttps://orcid.org/0000-0001-5135-4800

Biography

Ali Gooya is currently a Senior Lecturer in the School of Computing Science (IDA-Section) at the University of Glasgow, Scotland, UK. Before joining the University of Glasgow in 2022, he was an Associate Professor in the School of Computing at the University of Leeds (2018-2022) and the University of Sheffield (2016-2018). He has won multiple prestigious fellowships, including Allen Touring Institute (2022), JSPS short-term invitational (2020), FP7 Marie-Curie IIF fellowship (2014), and Japan Society for Promotion of Science JSPS-PDRA (2008). He earned his PhD from the University of Tokyo in medical image analysis (2007). He joined the University of Pennsylvania as a post-doc research associate to develop machine learning methods for medical vision (2008-2011).

Research interests

My research interest broadly lies in machine learning, computer vision and medical imaging. I am particularly keen on deep probabilistic learning with the target applications in cancer and cardiac image analysis, computer-aided decision support systems, prediction and marker discovery, statistical inference on populations, and computational anatomy.

My research vision is to aspire to unsupervised machine learning for AI in healthcare, as expert annotations in this particular field are sparse. I am experienced in creating methodologically innovative deep Bayesian frameworks, often involving rigorous mathematical modelling.

Publications

List by: Type | Date

Jump to: 2023 | 2022 | 2020 | 2019 | 2018 | 2017 | 2016
Number of items: 27.

2023

Elhaminia, B., Gilbert, A., Lilley, J., Abdar, M., Frangi, A. F., Scarsbrook, A., Appelt, A. and Gooya, A. (2023) Toxicity prediction in pelvic radiotherapy using multiple instance learning and cascaded attention layers. IEEE Journal of Biomedical and Health Informatics, 27(4), pp. 1958-1966. (doi: 10.1109/JBHI.2023.3238825)

Zakeri, A., Hokmabadi, A., Bi, N., Wijesinghe, I., Nix, M. G., Petersen, S. E., Frangi, A. F., Taylor, Z. A. and Gooya, A. (2023) DragNet: learning-based deformable registration for realistic cardiac MR sequence generation from a single frame. Medical Image Analysis, 83, 102678. (doi: 10.1016/j.media.2022.102678) (PMID:36403308)

2022

Appelt, A.L., Elhaminia, B., Gooya, A. , Gilbert, A. and Nix, M. (2022) Deep learning for radiotherapy outcome prediction using dose data – a review. Clinical Oncology, 34(2), e87-e96. (doi: 10.1016/j.clon.2021.12.002) (PMID:34924256)

Zakeri, A., Hokmabadi, A., Ravikumar, N., Frangi, A. F. and Gooya, A. (2022) A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment. Medical Image Analysis, 75, 102276. (doi: 10.1016/j.media.2021.102276) (PMID:34753021)

2020

Kumar, N. et al. (2020) A multi-organ nucleus segmentation challenge. IEEE Transactions on Medical Imaging, 39(5), pp. 1380-1391. (doi: 10.1109/TMI.2019.2947628) (PMID:31647422)

2019

Burgos, N., Gooya, A. and Svoboda, D. (2019) Preface. In: Burgos, N., Gooya, A. and Svoboda, D. (eds.) Simulation and Synthesis in Medical Imaging. Series: Lecture notes in computer science (11827). Springer, v-vi. ISBN 9783030327774

Alemi Koohbanani, N., Jahanifar, M., Gooya, A. and Rajpoot, N. (2019) Nuclear instance segmentation using a proposal-free spatially aware deep learning framework. In: Shen, D., Liu, T., Peters, T. M., Staib, L. H., Essert, C., Zhou, S., Yap, P.-T. and Khan, A. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Series: Lecture Notes in Computer Science, 11764. Springer: Cham, pp. 622-630. ISBN 9783030322380 (doi: 10.1007/978-3-030-32239-7_69)

Attar, R. et al. (2019) Quantitative CMR population imaging on 20,000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation. Medical Image Analysis, 56, pp. 26-42. (doi: 10.1016/j.media.2019.05.006) (PMID:31154149)

Zhang, L., Gooya, A. , Pereanez, M., Dong, B., Piechnik, S.K., Neubauer, S., Petersen, S.E. and Frangi, A.F. (2019) Automatic assessment of full left ventricular coverage in cardiac cine magnetic resonance imaging with Fisher-discriminative 3-D CNN. IEEE Transactions on Biomedical Engineering, 66(7), pp. 1975-1986. (doi: 10.1109/TBME.2018.2881952)

Ravikumar, N., Gooya, A. , Beltrachini, L., Frangi, A.F. and Taylor, Z.A. (2019) Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data. Medical Image Analysis, 53, pp. 47-63. (doi: 10.1016/j.media.2019.01.001) (PMID:30684740)

Jahanifar, M., Zamani Tajeddin, N., Mohammadzadeh Asl, B. and Gooya, A. (2019) Supervised saliency map driven segmentation of lesions in dermoscopic images. IEEE Journal of Biomedical and Health Informatics, 23(2), pp. 509-518. (doi: 10.1109/JBHI.2018.2839647) (PMID:29994323)

Attar, R., Pereañez, M., Gooya, A. , Albà, X., Zhang, L., Piechnik, S.K., Neubauer, S., Petersen, S.E. and Frangi, A.F. (2019) High throughput computation of reference ranges of biventricular cardiac function on the UK biobank population cohort. In: Pop, M., Sermesant, M., Zhao, J., Li, S., McLeod, K., Young, A., Rhode, K. and Mansi, T. (eds.) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. Series: Lecture Notes in Computer Science, 11395. Springer: Cham, pp. 114-121. ISBN 9783030120283 (doi: 10.1007/978-3-030-12029-0_13)

Fehri, H., Gooya, A. , Lu, Y., Meijering, E., Johnston, S.A. and Frangi, A.F. (2019) Bayesian polytrees with learned deep features for multi-class cell segmentation. IEEE Transactions on Image Processing, 28(7), pp. 3246-3260. (doi: 10.1109/TIP.2019.2895455)

2018

Gooya, A. , Goksel, O., Oguz, I. and Burgos, N. (2018) Preface. In: Gooya, A., Oguz, I., Goksel, O. and Burgos, N. (eds.) Simulation and Synthesis in Medical Imaging. Series: Lecture notes in computer science (11037). Springer, v-vi. ISBN 9783030005351

Nemat, H., Fehri, H., Ahmadinejad, N., Frangi, A.F. and Gooya, A. (2018) Classification of breast lesions in ultrasonography using sparse logistic regression and morphology-based texture features. Medical Physics, 45(9), pp. 4112-4124. (doi: 10.1002/mp.13082) (PMID:29974971)

Koohababni, N.A., Jahanifar, M., Gooya, A. and Rajpoot, N. (2018) Nuclei detection using mixture density networks. In: Shi, Y., Suk, H.-I. and Liu, M. (eds.) Machine Learning in Medical Imaging. Series: Lecture Notes in Computer Science, 11046. Springer: Cham, pp. 241-248. ISBN 9783030009182 (doi: 10.1007/978-3-030-00919-9_28)

Gooya, A. , Lekadir, K., Castro-Mateos, I., Pozo, J.M. and Frangi, A.F. (2018) Mixture of probabilistic principal component analyzers for shapes from point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), pp. 891-904. (doi: 10.1109/TPAMI.2017.2700276) (PMID:28475045)

Suinesiaputra, A. et al. (2018) Statistical shape modeling of the left ventricle: myocardial infarct classification challenge. IEEE Journal of Biomedical and Health Informatics, 22(2), pp. 503-515. (doi: 10.1109/JBHI.2017.2652449) (PMID:28103561) (PMCID:PMC5857476)

Ravikumar, N., Gooya, A. , Çimen, S., Frangi, A.F. and Taylor, Z.A. (2018) Group-wise similarity registration of point sets using Student's t-mixture model for statistical shape models. Medical Image Analysis, 44, pp. 156-176. (doi: 10.1016/j.media.2017.11.012) (PMID:29248842)

2017

Kalaie, S. and Gooya, A. (2017) Vascular tree tracking and bifurcation points detection in retinal images using a hierarchical probabilistic model. Computer Methods and Programs in Biomedicine, 151, pp. 139-149. (doi: 10.1016/j.cmpb.2017.08.018) (PMID:28946995)

Tsaftaris, S.A., Gooya, A. , Frangi, A.F. and Prince, J.L. (2017) Preface. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F. and Prince, J.L. (eds.) Simulation and Synthesis in Medical Imaging. Series: Lecture notes in computer science (10557). Springer, v-vi. ISBN 9783319681269

Zhang, L., Gooya, A. and Frangi, A.F. (2017) Semi-supervised assessment of incomplete LV coverage in cardiac MRI using generative adversarial nets. In: Tsaftaris, S. A., Gooya, A., Frangi, A. F. and Prince, J. L. (eds.) Simulation and Synthesis in Medical Imaging. Series: Lecture notes in computer science (10557). Springer, pp. 61-68. ISBN 9783319681269 (doi: 10.1007/978-3-319-68127-6_7)

Asl, M.E., Koohbanani, N.A., Frangi, A.F. and Gooya, A. (2017) Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform. Journal of Medical Imaging, 4(3), 034006. (doi: 10.1117/1.JMI.4.3.034006) (PMID:28924571) (PMCID:PMC5594385)

Ravikumar, N., Gooya, A. , Frangi, A.F. and Taylor, Z.A. (2017) Generalised coherent point drift for group-wise registration of multi-dimensional point sets. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D. L. and Duchesne, S. (eds.) Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. Series: Lecture Notes in Computer Science, 10433. Springer: Cham, pp. 309-316. ISBN 9783319661810 (doi: 10.1007/978-3-319-66182-7_36)

Shaukat, F., Raja, G., Gooya, A. and Frangi, A.F. (2017) Fully automatic detection of lung nodules in CT images using a hybrid feature set. Medical Physics, 44(7), pp. 3615-3629. (doi: 10.1002/mp.12273) (PMID:28409834)

Fehri, H., Gooya, A. , Johnston, S.A. and Frangi, A.F. (2017) Multi-class image segmentation in fluorescence microscopy using polytrees. In: Niethammer, M., Styner, M., Aylward, S., Zhu, H., Oguz, I., Yap, P.-T. and Shen, D. (eds.) Information Processing in Medical Imaging. Series: Lecture Notes in Computer Science, 10265. Springer: Cham, pp. 517-528. ISBN 9783319590493 (doi: 10.1007/978-3-319-59050-9_41)

2016

Peng, P., Lekadir, K., Gooya, A. , Shao, L., Petersen, S.E. and Frangi, A.F. (2016) A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Magnetic Resonance Materials in Physics, Biology and Medicine, 29(2), pp. 155-195. (doi: 10.1007/s10334-015-0521-4) (PMID:26811173) (PMCID:PMC4830888)

This list was generated on Thu Nov 30 23:55:55 2023 GMT.
Jump to: Articles | Book Sections
Number of items: 27.

Articles

Elhaminia, B., Gilbert, A., Lilley, J., Abdar, M., Frangi, A. F., Scarsbrook, A., Appelt, A. and Gooya, A. (2023) Toxicity prediction in pelvic radiotherapy using multiple instance learning and cascaded attention layers. IEEE Journal of Biomedical and Health Informatics, 27(4), pp. 1958-1966. (doi: 10.1109/JBHI.2023.3238825)

Zakeri, A., Hokmabadi, A., Bi, N., Wijesinghe, I., Nix, M. G., Petersen, S. E., Frangi, A. F., Taylor, Z. A. and Gooya, A. (2023) DragNet: learning-based deformable registration for realistic cardiac MR sequence generation from a single frame. Medical Image Analysis, 83, 102678. (doi: 10.1016/j.media.2022.102678) (PMID:36403308)

Appelt, A.L., Elhaminia, B., Gooya, A. , Gilbert, A. and Nix, M. (2022) Deep learning for radiotherapy outcome prediction using dose data – a review. Clinical Oncology, 34(2), e87-e96. (doi: 10.1016/j.clon.2021.12.002) (PMID:34924256)

Zakeri, A., Hokmabadi, A., Ravikumar, N., Frangi, A. F. and Gooya, A. (2022) A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment. Medical Image Analysis, 75, 102276. (doi: 10.1016/j.media.2021.102276) (PMID:34753021)

Kumar, N. et al. (2020) A multi-organ nucleus segmentation challenge. IEEE Transactions on Medical Imaging, 39(5), pp. 1380-1391. (doi: 10.1109/TMI.2019.2947628) (PMID:31647422)

Attar, R. et al. (2019) Quantitative CMR population imaging on 20,000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation. Medical Image Analysis, 56, pp. 26-42. (doi: 10.1016/j.media.2019.05.006) (PMID:31154149)

Zhang, L., Gooya, A. , Pereanez, M., Dong, B., Piechnik, S.K., Neubauer, S., Petersen, S.E. and Frangi, A.F. (2019) Automatic assessment of full left ventricular coverage in cardiac cine magnetic resonance imaging with Fisher-discriminative 3-D CNN. IEEE Transactions on Biomedical Engineering, 66(7), pp. 1975-1986. (doi: 10.1109/TBME.2018.2881952)

Ravikumar, N., Gooya, A. , Beltrachini, L., Frangi, A.F. and Taylor, Z.A. (2019) Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data. Medical Image Analysis, 53, pp. 47-63. (doi: 10.1016/j.media.2019.01.001) (PMID:30684740)

Jahanifar, M., Zamani Tajeddin, N., Mohammadzadeh Asl, B. and Gooya, A. (2019) Supervised saliency map driven segmentation of lesions in dermoscopic images. IEEE Journal of Biomedical and Health Informatics, 23(2), pp. 509-518. (doi: 10.1109/JBHI.2018.2839647) (PMID:29994323)

Fehri, H., Gooya, A. , Lu, Y., Meijering, E., Johnston, S.A. and Frangi, A.F. (2019) Bayesian polytrees with learned deep features for multi-class cell segmentation. IEEE Transactions on Image Processing, 28(7), pp. 3246-3260. (doi: 10.1109/TIP.2019.2895455)

Nemat, H., Fehri, H., Ahmadinejad, N., Frangi, A.F. and Gooya, A. (2018) Classification of breast lesions in ultrasonography using sparse logistic regression and morphology-based texture features. Medical Physics, 45(9), pp. 4112-4124. (doi: 10.1002/mp.13082) (PMID:29974971)

Gooya, A. , Lekadir, K., Castro-Mateos, I., Pozo, J.M. and Frangi, A.F. (2018) Mixture of probabilistic principal component analyzers for shapes from point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), pp. 891-904. (doi: 10.1109/TPAMI.2017.2700276) (PMID:28475045)

Suinesiaputra, A. et al. (2018) Statistical shape modeling of the left ventricle: myocardial infarct classification challenge. IEEE Journal of Biomedical and Health Informatics, 22(2), pp. 503-515. (doi: 10.1109/JBHI.2017.2652449) (PMID:28103561) (PMCID:PMC5857476)

Ravikumar, N., Gooya, A. , Çimen, S., Frangi, A.F. and Taylor, Z.A. (2018) Group-wise similarity registration of point sets using Student's t-mixture model for statistical shape models. Medical Image Analysis, 44, pp. 156-176. (doi: 10.1016/j.media.2017.11.012) (PMID:29248842)

Kalaie, S. and Gooya, A. (2017) Vascular tree tracking and bifurcation points detection in retinal images using a hierarchical probabilistic model. Computer Methods and Programs in Biomedicine, 151, pp. 139-149. (doi: 10.1016/j.cmpb.2017.08.018) (PMID:28946995)

Asl, M.E., Koohbanani, N.A., Frangi, A.F. and Gooya, A. (2017) Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform. Journal of Medical Imaging, 4(3), 034006. (doi: 10.1117/1.JMI.4.3.034006) (PMID:28924571) (PMCID:PMC5594385)

Shaukat, F., Raja, G., Gooya, A. and Frangi, A.F. (2017) Fully automatic detection of lung nodules in CT images using a hybrid feature set. Medical Physics, 44(7), pp. 3615-3629. (doi: 10.1002/mp.12273) (PMID:28409834)

Peng, P., Lekadir, K., Gooya, A. , Shao, L., Petersen, S.E. and Frangi, A.F. (2016) A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Magnetic Resonance Materials in Physics, Biology and Medicine, 29(2), pp. 155-195. (doi: 10.1007/s10334-015-0521-4) (PMID:26811173) (PMCID:PMC4830888)

Book Sections

Burgos, N., Gooya, A. and Svoboda, D. (2019) Preface. In: Burgos, N., Gooya, A. and Svoboda, D. (eds.) Simulation and Synthesis in Medical Imaging. Series: Lecture notes in computer science (11827). Springer, v-vi. ISBN 9783030327774

Alemi Koohbanani, N., Jahanifar, M., Gooya, A. and Rajpoot, N. (2019) Nuclear instance segmentation using a proposal-free spatially aware deep learning framework. In: Shen, D., Liu, T., Peters, T. M., Staib, L. H., Essert, C., Zhou, S., Yap, P.-T. and Khan, A. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Series: Lecture Notes in Computer Science, 11764. Springer: Cham, pp. 622-630. ISBN 9783030322380 (doi: 10.1007/978-3-030-32239-7_69)

Attar, R., Pereañez, M., Gooya, A. , Albà, X., Zhang, L., Piechnik, S.K., Neubauer, S., Petersen, S.E. and Frangi, A.F. (2019) High throughput computation of reference ranges of biventricular cardiac function on the UK biobank population cohort. In: Pop, M., Sermesant, M., Zhao, J., Li, S., McLeod, K., Young, A., Rhode, K. and Mansi, T. (eds.) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. Series: Lecture Notes in Computer Science, 11395. Springer: Cham, pp. 114-121. ISBN 9783030120283 (doi: 10.1007/978-3-030-12029-0_13)

Gooya, A. , Goksel, O., Oguz, I. and Burgos, N. (2018) Preface. In: Gooya, A., Oguz, I., Goksel, O. and Burgos, N. (eds.) Simulation and Synthesis in Medical Imaging. Series: Lecture notes in computer science (11037). Springer, v-vi. ISBN 9783030005351

Koohababni, N.A., Jahanifar, M., Gooya, A. and Rajpoot, N. (2018) Nuclei detection using mixture density networks. In: Shi, Y., Suk, H.-I. and Liu, M. (eds.) Machine Learning in Medical Imaging. Series: Lecture Notes in Computer Science, 11046. Springer: Cham, pp. 241-248. ISBN 9783030009182 (doi: 10.1007/978-3-030-00919-9_28)

Tsaftaris, S.A., Gooya, A. , Frangi, A.F. and Prince, J.L. (2017) Preface. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F. and Prince, J.L. (eds.) Simulation and Synthesis in Medical Imaging. Series: Lecture notes in computer science (10557). Springer, v-vi. ISBN 9783319681269

Zhang, L., Gooya, A. and Frangi, A.F. (2017) Semi-supervised assessment of incomplete LV coverage in cardiac MRI using generative adversarial nets. In: Tsaftaris, S. A., Gooya, A., Frangi, A. F. and Prince, J. L. (eds.) Simulation and Synthesis in Medical Imaging. Series: Lecture notes in computer science (10557). Springer, pp. 61-68. ISBN 9783319681269 (doi: 10.1007/978-3-319-68127-6_7)

Ravikumar, N., Gooya, A. , Frangi, A.F. and Taylor, Z.A. (2017) Generalised coherent point drift for group-wise registration of multi-dimensional point sets. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D. L. and Duchesne, S. (eds.) Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. Series: Lecture Notes in Computer Science, 10433. Springer: Cham, pp. 309-316. ISBN 9783319661810 (doi: 10.1007/978-3-319-66182-7_36)

Fehri, H., Gooya, A. , Johnston, S.A. and Frangi, A.F. (2017) Multi-class image segmentation in fluorescence microscopy using polytrees. In: Niethammer, M., Styner, M., Aylward, S., Zhu, H., Oguz, I., Yap, P.-T. and Shen, D. (eds.) Information Processing in Medical Imaging. Series: Lecture Notes in Computer Science, 10265. Springer: Cham, pp. 517-528. ISBN 9783319590493 (doi: 10.1007/978-3-319-59050-9_41)

This list was generated on Thu Nov 30 23:55:55 2023 GMT.

Grants

  • EPSRC Impact Acceleration Award (as PI)
  • EPSRC New Investigator Grant (as PI, EP/S012796/1)
  • 2 x Cancer Research UK sandpit awards on early detection of cancer
  • Innovate UK KTP application (as Co-PI)
  • Marie-Curie IIF Fellowship
  • JSPS Short Invitational Fellowship
  • JSPS PDRA Fellowship
  • 2x NVIDIA GPU grant in aid

Supervision

I am looking for PhD students interested in Deep Learning, Probabilistic and Generative Modelling applied to Medical Imaging and patient meta-data. Potential project directions are listed below, and other projects are due. The candidate is expected to have strong analytical and math skills, good programming experience, some prior experience in machine learning and visual computing, and good English communication skills. Please get in touch with me (ali.gooya@glasgow.ac.uk) for further information.

Bayesian Deep Atlases for Cardiac Motion Abnormality Assessment by Integrating  Imaging and Metadata 

Cardiovascular diseases (CVDs) are the second biggest killer in the UK; currently, more than 7 million people live with CVD in the country. Early identification of individuals with significant risk is critical to improve the patient's quality of life and reduce the financial burden on the social and healthcare systems. Many CVDs lead to a shortage of blood supply to the heart muscle, and abnormal motion is diagnosed non-invasively by analysing the patient's dynamic cardiac imaging data. Manual assessment of these images is subjective, non-reproducible, limited to the left ventricle, and time-consuming. Statistical atlases, describing the 'average' pattern of the heart motion over a sizeable healthy population, can be potentially helpful in identifying deviations from normality in individuals. However, the integration of the existing atlases into clinical practice is inhibited by three fundamental limitations: (i) the derived motion statistics are often independent of the patient's age, gender, weight, etc. (metadata) that are essential for precise diagnosis, (ii) the detected abnormalities due to failure of heart segmentation could not be disentangled from the underlying clinical conditions.

To alleviate these fundamental limitations, this proposal aims, for the first time, to develop a complete probabilistic atlas to evaluate bi-ventricular motion abnormalities accurately.

  • Holistically integrating imaging and metadata from a large population cardiac imaging study.
  • Disentangling the algorithmic segmentation failures from underlying clinical conditions
  • Addressing the computational challenge of extending deep transformer models to motion data

The framework will be a novel Bayesian approach extending the recent developments in deep recurrent neural networks (e.g. Vision Transformers). These networks provide a natural mechanism to model sequential data such as 2D video. Yet, using Transformers to model the complex dynamics of the heart motion is conceptually new and powerful. The motion will be modelled as the spatiotemporal (3D+t) sequence of the heart shapes across the cardiac cycle, extracted from cine Cardiac Magnetic Resonance (CMR) images. The atlas will be a recurrent model that, given a sequence, will predict a probabilistic distribution function (pdf) for the following heart status. The critical aspect is that the pdf will be conditioned on the patient's metadata (age, gender, ethnicity, etc.). Thus by measuring the spatial deviations from the expected shape at each phase, the atlas will allow very accurate quantification of anatomical and functional cardiac abnormalities (and variances showing uncertainties) specific to the patients.

We have extensive experience developing Bayesian and non-Gaussian statistical atlases from cardiac shapes and motion. However, the previous work discarded the patient metadata (such as age, gender, ethnicity, etc.). Therefore, the atlas was not clinically deployable to study cardiac motion abnormalities, which are relevant to various CVDs. 

The atlas will be derived from the UK Biobank CMR study aiming to scan 100,000 patients by 2022. The training of the atlas will be pursued as the new releases of the data sets from the UK Biobank becomes available. We have established collaboration with this study's clinical advisor and have full access to the CMR data sets. 

Teaching

COMPSCI1020 How to learn a new language? 

 

COMPSCI4015 Professional Software Development 

Professional activities & recognition

Prizes, awards & distinctions

  • 2022: Short-Term, Tokyo Women Medical University (JSPS Invitational Fellowship)
  • 2019: Early Detection Seed Grants (Cancer Research UK)
  • 2020: Vet-AI/University of Leeds (Innovate-UK)

Research fellowships

  • 2021 - 2022: Allen Touring Institue
  • 2014 - 2016: Marie Skłodowska-Curie Individual Fellowship
  • 2008 - 2010: JSPS Fellowship

Editorial boards

  • 2017 - 2019: MICCAI-SASHIMI Workshop