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

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

Search Professor Shakes Chandra’s works on UQ eSpace

96 works between 2006 and 2025
All (96) Journal Article (48) Conference Publication (45) Other Outputs (3)

1 - 20 of 96 works

2025

Journal Article

SCGC : Self-supervised contrastive graph clustering

Kulatilleke, Gayan K., Portmann, Marius and Chandra, Shekhar S. (2025). SCGC : Self-supervised contrastive graph clustering. Neurocomputing, 611 128629, 128629. doi: 10.1016/j.neucom.2024.128629

SCGC : Self-supervised contrastive graph clustering

2024

Journal Article

An automated and robust tool for musculoskeletal and finite element modeling of the knee joint

Esrafilian, Amir, Chandra, Shekhar S, Gatti, Anthony A, Nissi, Mikko, Mustonen, Anne-Mari, Saisanen, Laura, Reijonen, Jusa, Nieminen, Petteri, Julkunen, Petro, Toyras, Juha, Saxby, David J, Lloyd, David G and Korhonen, Rami K (2024). An automated and robust tool for musculoskeletal and finite element modeling of the knee joint. IEEE Transactions on Biomedical Engineering, 1-13. doi: 10.1109/tbme.2024.3438272

An automated and robust tool for musculoskeletal and finite element modeling of the knee joint

2024

Conference Publication

A Tiered Quadruplet Network with Patient-Specific Mining and Dynamic Margin for Improved ugly Duckling Lesion Classification

Naranpanawa, Nathasha, Soyer, H. Peter, Mothershaw, Adam, Kulatilleke, Gayan K., Ge, Zongyuan, Betz-Stablein, Brigid and Chandra, Shekhar S. (2024). A Tiered Quadruplet Network with Patient-Specific Mining and Dynamic Margin for Improved ugly Duckling Lesion Classification. IEEE. doi: 10.1109/isbi56570.2024.10635546

A Tiered Quadruplet Network with Patient-Specific Mining and Dynamic Margin for Improved ugly Duckling Lesion Classification

2024

Conference Publication

Single Image Compressed Sensing MRI Via a Self-Supervised Deep Denoising Approach

Lorenzana, Marlon Bran, Liu, Feng and Chandra, Shekhar S. (2024). Single Image Compressed Sensing MRI Via a Self-Supervised Deep Denoising Approach. IEEE. doi: 10.1109/isbi56570.2024.10635749

Single Image Compressed Sensing MRI Via a Self-Supervised Deep Denoising Approach

2024

Journal Article

Automated Detection of Pigmented Iris Freckles using a Deep Neural Network for Cutaneous Melanoma Risk

Naranpanawa, Nathasha, Jayasinghe, Dilki, Sturm, Richard A., Betz-Stablein, Brigid, Janda, Monika, Eriksson, Anders, Soyer, H. Peter and Chandra, Shekhar S. (2024). Automated Detection of Pigmented Iris Freckles using a Deep Neural Network for Cutaneous Melanoma Risk. Journal of Investigative Dermatology. doi: 10.1016/j.jid.2024.04.029

Automated Detection of Pigmented Iris Freckles using a Deep Neural Network for Cutaneous Melanoma Risk

2024

Journal Article

Automatic segmentation of tumour and organs at risk in 3D MRI for cervical cancer radiation therapy with anatomical variations

Leung, Sze-Nung, Chandra, Shekhar S., Lim, Karen, Young, Tony, Holloway, Lois and Dowling, Jason A. (2024). Automatic segmentation of tumour and organs at risk in 3D MRI for cervical cancer radiation therapy with anatomical variations. Physical and Engineering Sciences in Medicine, 47 (3), 1-10. doi: 10.1007/s13246-024-01415-y

Automatic segmentation of tumour and organs at risk in 3D MRI for cervical cancer radiation therapy with anatomical variations

2024

Journal Article

Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative

Woo, Boyeong, Engstrom, Craig, Baresic, William, Fripp, Jurgen, Crozier, Stuart and Chandra, Shekhar S. (2024). Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative. Medical Image Analysis, 93 103089, 103089. doi: 10.1016/j.media.2024.103089

Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative

2024

Journal Article

Analysis of cam location characteristics in FAI syndrome patients from 3D MR images demonstrates sex‐specific differences

Bugeja, Jessica M., Xia, Ying, Chandra, Shekhar S., Murphy, Nicholas J., Crozier, Stuart, Hunter, David J., Fripp, Jurgen and Engstrom, Craig (2024). Analysis of cam location characteristics in FAI syndrome patients from 3D MR images demonstrates sex‐specific differences. Journal of Orthopaedic Research, 42 (2), 385-394. doi: 10.1002/jor.25674

Analysis of cam location characteristics in FAI syndrome patients from 3D MR images demonstrates sex‐specific differences

2024

Journal Article

GrainPointNet: a deep-learning framework for non-invasive sorghum panicle grain count phenotyping

James, Chrisbin, Smith, Daniel, He, Weigao, Chandra, Shekhar S. and Chapman, Scott C. (2024). GrainPointNet: a deep-learning framework for non-invasive sorghum panicle grain count phenotyping. Computers and Electronics in Agriculture, 217 108485, 108485. doi: 10.1016/j.compag.2023.108485

GrainPointNet: a deep-learning framework for non-invasive sorghum panicle grain count phenotyping

2024

Journal Article

AliasNet: Alias artefact suppression network for accelerated phase-encode MRI

Bran Lorenzana, Marlon, Chandra, Shekhar S. and Liu, Feng (2024). AliasNet: Alias artefact suppression network for accelerated phase-encode MRI. Magnetic Resonance Imaging, 105, 17-28. doi: 10.1016/j.mri.2023.10.001

AliasNet: Alias artefact suppression network for accelerated phase-encode MRI

2024

Journal Article

Multi-modal traumatic brain injury prognosis via structure-aware field-wise learning

Zhang, Lu, Li, Zhibin, Chandra, Shekhar S. and Nasrallah, Fatima (2024). Multi-modal traumatic brain injury prognosis via structure-aware field-wise learning. IEEE Transactions on Knowledge and Data Engineering, 36 (8), 1-12. doi: 10.1109/tkde.2024.3364385

Multi-modal traumatic brain injury prognosis via structure-aware field-wise learning

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. 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

Current 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
    Osteoarthritis Compass: Personalized prediction of disease onset and progression. (NHMRC Ideas Grant administered by Griffith University)
    Griffith University
    Open grant
  • 2020 - 2025
    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

  • 2021 - 2024
    ChondralHealth Productization: Automated Musculoskeletal MR Image Analysis Algorithms
    Siemens Healthcare Pty Ltd
    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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