Dr Shakes Chandra
Dr

Shakes Chandra

Email: 
Phone: 
+61 7 336 58359

Background

Shakes an imaging expert that leads a strong deep learning, artificial intelligence (AI) focused research team interested in medical image analysis and signal/image processing applied to many areas of science and medicine. He received his Ph.D in Theoretical Physics from Monash University, Melbourne and has been involved in applying machine learning in medical imaging for over a decade.

Shakes’ past work has involved developing shape model-based algorithms for knee, hip and shoulder joint segmentation that is being developed and deployed as a product on the Siemens syngo.via platform. More recent work involves deep learning based algorithms for semantic segmentation and manifold learning of imaging data. Broadly, he is interested in understanding and developing the mathematical basis of imaging, image analysis algorithms and physical systems. He has developed algorithms that utilise exotic mathematical structures such as fractals, turbulence, group theoretic concepts and number theory in the image processing approaches that he has developed.

He is currently a Senior Lecturer and leads a team of 20+ researchers working image analysis and AI research across healthcare and medicine. He currently teaches the computer science courses Theory of Computation and Pattern Recognition and Analysis.

Availability

Dr Shakes Chandra is:
Available for supervision

Fields of research

Artificial intelligence Biomedical engineering Biomedical imaging Combinatorics and discrete mathematics (excl. physical combinatorics) Computational imaging Computer vision and multimedia computation Deep learning Engineering Image processing Information and Computing Sciences Machine learning Mathematical Sciences Pure mathematics Theory of computation

Qualifications

  • Doctor of Philosophy, Monash University

Research interests

  • Magnetic Resonance Imaging

    Making MRI faster and more affordable through better image reconstruction, processing and analysis.

  • Image Processing

    Image reconstruction, segmentation and registration.

  • Deep learning

    Dimensionality reduction, machine learning and Artificial Intelligence

  • Fractals and Chaos

    Applying fractals and chaos to image processing and computer science.

  • Number Theory

    Applying number theory to image processing and computer science.

  • Medical Image Analysis

    Medical image segmentation and shape analysis

Research impacts

Times Higher Education - A joint effort: machine learning’s search for the first MRI-based biomarker of osteoarthritis

UQNews - Making magnetic resonance imaging (MRI) faster using fractals and turbulence.

Search Professor Shakes Chandra’s works on UQ eSpace

105 works between 2006 and 2025
All (105) Journal Article (52) Other Outputs (5) Conference Publication (48)

21 - 40 of 105 works

2024

Conference Publication

Interpretable 3D multi-modal residual convolutional neural network for mild traumatic brain injury diagnosis

Ellethy, Hanem, Vegh, Viktor and Chandra, Shekhar S. (2024). Interpretable 3D multi-modal residual convolutional neural network for mild traumatic brain injury diagnosis. 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, 28 November – 1 December 2023. Singapore, Singapore: Springer Nature Singapore. doi: 10.1007/978-981-99-8388-9_39

Interpretable 3D multi-modal residual convolutional neural network for mild traumatic brain injury diagnosis

2024

Other Outputs

Pigmented Iris Freckle Data 

Naranpanawa, Nathasha, Chandra, Shekhar S. and Sturm, Richard A. (2024). Pigmented Iris Freckle Data . The University of Queensland. (Dataset) doi: 10.48610/355ad45

Pigmented Iris Freckle Data 

2024

Conference Publication

An unsupervised deep learning-based method for in vivo high resolution Kidney MRI motion correction

Moinian, Shahrzad, Kurniawan, Nyoman, Chandra, Shekhar, Vegh, Viktor and Reutens, David (2024). An unsupervised deep learning-based method for in vivo high resolution Kidney MRI motion correction. 2023 ISMRM & ISMRT Annual Meeting & Exhibition, Toronto, ON, Canada, 3-8 June 2023. Berkeley, CA, United States: International Society for Magnetic Resonance in Medicine. doi: 10.58530/2023/4915

An unsupervised deep learning-based method for in vivo high resolution Kidney MRI motion correction

2023

Journal Article

Efficient block contrastive learning via parameter-free meta-node approximation

Kulatilleke, Gayan K., Portmann, Marius and Chandra, Shekhar S. (2023). Efficient block contrastive learning via parameter-free meta-node approximation. Neurocomputing, 561 126850, 126850. doi: 10.1016/j.neucom.2023.126850

Efficient block contrastive learning via parameter-free meta-node approximation

2023

Conference Publication

Manipulating medical image translation with manifold disentanglement

Liu, Siyu, Dowling, Jason A., Engstrom, Craig, Greer, Peter B., Crozier, Stuart and Chandra, Shekhar S. (2023). Manipulating medical image translation with manifold disentanglement. 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Port Macquarie, Australia, 28 November-1 December 2023. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/dicta60407.2023.00053

Manipulating medical image translation with manifold disentanglement

2023

Conference Publication

Suspicious naevi classification using auxiliary classifier generative adversarial network

Zegair, Fatima Al, Rutjes, Chantal, Betz-Stablein, Brigid, Ge, Zongyuan, Soyer, H. Peter and Chandra, Shekhar S. (2023). Suspicious naevi classification using auxiliary classifier generative adversarial network. 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Port Macquarie, NSW, Australia, 28 November - 1 December 2023. Piscataway, NJ, United States: IEEE. doi: 10.1109/dicta60407.2023.00041

Suspicious naevi classification using auxiliary classifier generative adversarial network

2023

Conference Publication

TriFormer: A multi-modal transformer framework for mild cognitive impairment conversion prediction

Liu, Linfeng, Lyu, Junyan, Liu, Siyu, Tang, Xiaoying, Chandra, Shekhar S. and Nasrallah, Fatima A. (2023). TriFormer: A multi-modal transformer framework for mild cognitive impairment conversion prediction. 20th IEEE International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia, 18-21 April 2023. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/isbi53787.2023.10230709

TriFormer: A multi-modal transformer framework for mild cognitive impairment conversion prediction

2023

Journal Article

Non-separable two-dimensional Hadamard transform via a discrete Hadamard slice theorem

Lorenzana, Marlon Bran and Chandra, Shekhar S. (2023). Non-separable two-dimensional Hadamard transform via a discrete Hadamard slice theorem. IEEE Signal Processing Letters, 30 (99), 1237-1241. doi: 10.1109/lsp.2023.3311349

Non-separable two-dimensional Hadamard transform via a discrete Hadamard slice theorem

2023

Conference Publication

Medical shape pattern analysis with MeshCNN

Leung, Sze-Nung, Dowling, Jason A., Fripp, Jurgen, Shen, Kai-Kai and Chandra, Shekhar S. (2023). Medical shape pattern analysis with MeshCNN. 20th IEEE International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia, 18-21 April 2023. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/isbi53787.2023.10230427

Medical shape pattern analysis with MeshCNN

2023

Journal Article

An unsupervised deep learning-based image translation method for retrospective motion correction of high resolution kidney MRI

Moinian, Shahrzad, Kurniawan, Nyoman D., Chandra, Shekhar S., Vegh, Viktor and Reutens, David C. (2023). An unsupervised deep learning-based image translation method for retrospective motion correction of high resolution kidney MRI. Intelligence-Based Medicine, 8 100108, 1-15. doi: 10.1016/j.ibmed.2023.100108

An unsupervised deep learning-based image translation method for retrospective motion correction of high resolution kidney MRI

2023

Journal Article

Cascaded multi-modal mixing transformers for Alzheimer’s disease classification with incomplete data

Liu, Linfeng, Liu, Siyu, Zhang, Lu, To, Xuan Vinh, Nasrallah, Fatima and Chandra, Shekhar S. (2023). Cascaded multi-modal mixing transformers for Alzheimer’s disease classification with incomplete data. NeuroImage, 277 120267, 120267. doi: 10.1016/j.neuroimage.2023.120267

Cascaded multi-modal mixing transformers for Alzheimer’s disease classification with incomplete data

2023

Conference Publication

Towards trustable skin cancer diagnosis via rewriting model's decision

Yan, Siyuan, Yu, Zhen, Zhang, Xuelin, Mahapatra, Dwarikanath, Chandra, Shekhar S., Janda, Monika, Soyer, Peter and Ge, Zongyuan (2023). Towards trustable skin cancer diagnosis via rewriting model's decision. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 17-24 June 2023. Piscataway, NJ, United States: IEEE. doi: 10.1109/cvpr52729.2023.01113

Towards trustable skin cancer diagnosis via rewriting model's decision

2023

Journal Article

PREdiction and Diagnosis using Imaging and Clinical biomarkers Trial in Traumatic Brain Injury (PREDICT-TBI) study protocol: an observational, prospective, multicentre cohort study for the prediction of outcome in moderate-to-severe TBI

Nasrallah, Fatima, Bellapart, Judith, Walsham, James, Jacobson, Esther, To, Xuan Vinh, Manzanero, Silvia, Brown, Nathan, Meyer, Jason, Stuart, Janine, Evans, Tracey, Chandra, Shekhar S., Ross, Jason, Campbell, Lewis, Senthuran, Siva, Newcombe, Virginia, McCullough, James, Fleming, Jennifer, Pollard, Clifford and Reade, Michael (2023). PREdiction and Diagnosis using Imaging and Clinical biomarkers Trial in Traumatic Brain Injury (PREDICT-TBI) study protocol: an observational, prospective, multicentre cohort study for the prediction of outcome in moderate-to-severe TBI. BMJ Open, 13 (4) e067740, 1-9. doi: 10.1136/bmjopen-2022-067740

PREdiction and Diagnosis using Imaging and Clinical biomarkers Trial in Traumatic Brain Injury (PREDICT-TBI) study protocol: an observational, prospective, multicentre cohort study for the prediction of outcome in moderate-to-severe TBI

2023

Conference Publication

Semantic segmentation of 3D medical images through a kaleidoscope: data from the Osteoarthritis Initiative

Woo, Boyeong, Bran Lorenzana, Marlon, Engstrom, Craig, Baresic, William, Fripp, Jurgen, Crozier, Stuart and Chandra, Shekhar S. (2023). Semantic segmentation of 3D medical images through a kaleidoscope: data from the Osteoarthritis Initiative. Medical Imaging with Deep Learning, Nashville, TN, United States, 10-12 July 2023. Cambridge, MA, United States: ML Research Press.

Semantic segmentation of 3D medical images through a kaleidoscope: data from the Osteoarthritis Initiative

2023

Conference Publication

Semantic segmentation of 3D medical images through a kaleidoscope: data from the Osteoarthritis Initiative

Woo, Boyeong, Bran Lorenzanal, Marlon, Engstrom, Craig, Baresic, William, Fripp, Jurgen, Crozier, Stuart and Chandra, Shekhar S. (2023). Semantic segmentation of 3D medical images through a kaleidoscope: data from the Osteoarthritis Initiative. 6th International Conference on Medical Imaging with Deep Learning (MIDL), Nashville, TN, United States, 10-12 July 2023. San Diego, CA, United States: JMLR-Journal of Machine Learning Research.

Semantic segmentation of 3D medical images through a kaleidoscope: data from the Osteoarthritis Initiative

2023

Conference Publication

Style-based manifold for weakly-supervised disease characteristic discovery

Liu, Siyu, Liu, Linfeng, Engstrom, Craig, To, Xuan Vinh, Ge, Zongyuan, Crozier, Stuart, Nasrallah, Fatima and Chandra, Shekhar S. (2023). Style-based manifold for weakly-supervised disease characteristic discovery. MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, 8-12 October 2023. Heidelberg, Germany: Springer. doi: 10.1007/978-3-031-43904-9_36

Style-based manifold for weakly-supervised disease characteristic discovery

2022

Other Outputs

Osteoarthritis Initiative (OAI) - UQ

Woo, Boyeong , Chandra, Shekhar S. , Engstrom, Craig and Crozier, Stuart (2022). Osteoarthritis Initiative (OAI) - UQ. The University of Queensland. (Dataset) doi: 10.48610/d8e13fb

Osteoarthritis Initiative (OAI) - UQ

2022

Conference Publication

Transformer compressed sensing via global image tokens

Bran Lorenzana, Marlon, Engstrom, Craig and Chandra, Shekhar S. (2022). Transformer compressed sensing via global image tokens. 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France, 16-19 October 2022. Piscataway, NJ, United States: IEEE. doi: 10.1109/icip46576.2022.9897630

Transformer compressed sensing via global image tokens

2022

Conference Publication

Skin lesion recognition with class-hierarchy regularized hyperbolic embeddings

Yu, Zhen, Nguyen, Toan, Gal, Yaniv, Ju, Lie, Chandra, Shekhar S., Zhang, Lei, Bonnington, Paul, Mar, Victoria, Wang, Zhiyong and Ge, Zongyuan (2022). Skin lesion recognition with class-hierarchy regularized hyperbolic embeddings. 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Singapore, Singapore, 18-22 September 2022. Heidelberg, Germany: Springer. doi: 10.1007/978-3-031-16437-8_57

Skin lesion recognition with class-hierarchy regularized hyperbolic embeddings

2022

Journal Article

CAN3D: Fast 3D medical image segmentation via compact context aggregation

Dai, Wei, Woo, Boyeong, Liu, Siyu, Marques, Matthew, Engstrom, Craig, Greer, Peter B., Crozier, Stuart, Dowling, Jason A. and Chandra, Shekhar S. (2022). CAN3D: Fast 3D medical image segmentation via compact context aggregation. Medical Image Analysis, 82 102562, 1-17. doi: 10.1016/j.media.2022.102562

CAN3D: Fast 3D medical image segmentation via compact context aggregation

Current funding

  • 2026 - 2029
    Next generation magnetic resonance imaging through vision
    ARC Future Fellowships
    Open grant
  • 2025 - 2027
    Cost effective and portable low-field musculoskeletal MRI for high performance sport
    Australia's Economic Accelerator Innovate Grants
    Open grant
  • 2020 - 2026
    PREDICT-TBI - PREdiction and Diagnosis using Imaging and Clinical biomarkers Trial in Traumatic Brain Injury: the value of Magnetic Resonance Imaging
    NHMRC MRFF Traumatic Brain Injury Mission
    Open grant

Past funding

  • 2022 - 2025
    Advancing the visualisation and quantification of nephrons with MRI
    ARC Discovery Projects
    Open grant
  • 2022 - 2025
    Robust, valid and interpretable deep learning for quantitative imaging
    ARC Linkage Projects
    Open grant
  • 2021 - 2024
    ChondralHealth Productization: Automated Musculoskeletal MR Image Analysis Algorithms
    Siemens Healthcare Pty Ltd
    Open grant
  • 2021 - 2024
    Osteoarthritis Compass: Personalized prediction of disease onset and progression. (NHMRC Ideas Grant administered by Griffith University)
    Griffith University
    Open grant
  • 2018 - 2022
    MR Hip Intervention and Planning System to enhance clinical and surgical outcomes
    NHMRC Development Grant
    Open grant

Availability

Dr Shakes Chandra is:
Available for supervision

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Available projects

  • Machine learning applied to 3D magnetic resonance images

    Magnetic resonance (MR) imaging has become an important non-invasive radiological modality for various clinical applications, such as cartilage assessment for Osteoarthritis and treatment planning for prostate cancer. MR images in 3D, while providing a wealth of anatomical information, including bones and soft tissue, are difficult to analyse due to the presence of a large number of complex structures. Thus, extracting meaningful clinical information without human interaction is a challenging task. Developing such automatic methods are important in order to reduce human errors and the time taken by clinicians in completing mundane tasks, such as marking or delineating 3D images by hand, from hours to just a few minutes by utilising computers.

    In this project, the student will develop novel algorithms to solve segmentation and detection problems for MR imaging that could possibly be deployed to MRI scanners and may eventually used for diagnostic purposes. The project will involve applying computer vision and machine learning techniques (including deep learning) to MR image processing and analysis.

Supervision history

Current supervision

Completed supervision

Enquiries

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