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)

1 - 20 of 105 works

2025

Other Outputs

SAMRI

Zhao Wang and Shakes Chandra (2025). SAMRI. The University of Queensland. (Dataset) doi: 10.48610/cadac84

SAMRI

2025

Journal Article

A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers

Ghahari, Nima, Caffery, Liam, Betz-Stablein, Brigid, Mothershaw, Adam, Jayasinghe, Dilki, Primiero, Clare, Chandra, Shekhar S., Torrano, Joachim, Soyer, H. Peter and Janda, Monika (2025). A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers. Scientific Data, 12 (1) 1602, 1602-1. doi: 10.1038/s41597-025-05880-2

A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers

2025

Journal Article

Identifying suspicious naevi with dermoscopy via variational autoencoder auxiliary generative classifiers

Al Zegair, Fatima, Betz-Stablein, Brigid, Janda, Monika, Soyer, H. Peter and Chandra, Shekhar S. (2025). Identifying suspicious naevi with dermoscopy via variational autoencoder auxiliary generative classifiers. Physical and Engineering Sciences in Medicine. doi: 10.1007/s13246-025-01636-9

Identifying suspicious naevi with dermoscopy via variational autoencoder auxiliary generative classifiers

2025

Journal Article

A scalable and efficient UAV-based pipeline and deep learning framework for phenotyping sorghum panicle morphology from point clouds

James, Chrisbin, Chandra, Shekhar S. and Chapman, Scott C. (2025). A scalable and efficient UAV-based pipeline and deep learning framework for phenotyping sorghum panicle morphology from point clouds. Plant Phenomics, 7 (2) 100050, 1-19. doi: 10.1016/j.plaphe.2025.100050

A scalable and efficient UAV-based pipeline and deep learning framework for phenotyping sorghum panicle morphology from point clouds

2025

Conference Publication

Automated 3D segmentation of glomeruli in human kidney tissue specimens using 16.4 T MRI without contrast agents

Amar, Aurel J., Kurniawan, Nyoman D., Cullen-McEwen, Luise A., Kassianos, Andrew J., Healy, Helen G., Bertram, John F., Chandra, Shekhar S. and Reutens, David C. (2025). Automated 3D segmentation of glomeruli in human kidney tissue specimens using 16.4 T MRI without contrast agents. 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI), Houston, TX, United States, 14-17 April 2025. Piscataway, NJ, United States: IEEE. doi: 10.1109/isbi60581.2025.10980815

Automated 3D segmentation of glomeruli in human kidney tissue specimens using 16.4 T MRI without contrast agents

2025

Journal Article

Hybrid discrete and finite element analysis enables fast evaluation of hip joint cartilage mechanical response

Venäläinen, Mikko S., Li, Mao, Töyräs, Juha, Korhonen, Rami K., Fripp, Jurgen, Crozier, Stuart, Chandra, Shekhar S. and Engstrom, Craig (2025). Hybrid discrete and finite element analysis enables fast evaluation of hip joint cartilage mechanical response. Journal of Biomechanics, 182 112568, 1-6. doi: 10.1016/j.jbiomech.2025.112568

Hybrid discrete and finite element analysis enables fast evaluation of hip joint cartilage mechanical response

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

2025

Other Outputs

A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers

Ghahari, Nima, Caffery, Liam, Betz-Stablein, Brigid, Mothershaw, Adam, Jayasinghe, Dilki, Primiero, Clare, Chandra, Shekhar S., Torrano, Joachim, Soyer, H. Peter and Janda, Monika (2025). A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers. The University of Queensland. (Dataset) doi: 10.48610/a13deaf

A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers

2025

Conference Publication

Anatomical grounding pre-training for medical phrase grounding

Zhang, Wenjun, Chandra, Shekhar S. and Nicolson, Aaron (2025). Anatomical grounding pre-training for medical phrase grounding. 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI), Houston, TX, United States, 14-17 April 2025. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ISBI60581.2025.10980652

Anatomical grounding pre-training for medical phrase grounding

2024

Conference Publication

Evidence-aware multi-modal data fusion and its application to total knee replacement prediction

Liu, Xinwen, Wang, Jing, Zhou, S. Kevin, Engstrom, Craig and Chandra, Shekhar S. (2024). Evidence-aware multi-modal data fusion and its application to total knee replacement prediction. 2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Perth, Australia, 27-29 November 2024. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/dicta63115.2024.00042

Evidence-aware multi-modal data fusion and its application to total knee replacement prediction

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, 72 (1), 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. 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 27-30 May 2024. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. 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

Bran Lorenzana, Marlon, Liu, Feng and Chandra, Shekhar S. (2024). Single image compressed sensing MRI via a self-supervised deep denoising approach. 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 27-30 May 2024. Piscataway, NJ, United States: 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, 144 (11), 2602-2605. 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

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

Looking for a supervisor? Read our advice on how to choose a supervisor.

Available projects

  • Next generation magnetic resonance imaging MRI through vision

    Summary: Magnetic resonance imaging (MRI) is crucial for diagnosing diseases within the human body. In this project, we develop new AI methods that leverage human visual perception to make MRI faster and more affordable.

    Technologies such as magnetic resonance imaging (MRI) are essential in healthcare for non-invasively seeing inside the human body for disease diagnosis and assessment. However, imaging cost for MRI is so prohibitive that it is seldom used unless there is no other option despite its effectiveness. The cost is largely because MRI is a slow imaging modality compared to other options that do not provide as much information and soft tissue contrast needed to detect diseases such as cancer. Although some progress has been made to improve acquisition speed, all current methods do not make any allowances for the way that human experts read and understand regions of interest. A reduction in scan time will make MRI cheaper and therefore allow the technology to be more readily utilised in the future.

    This project aims to create new artificial intelligence (AI) models and unify them with MRI acquisition directly in its measurement domain, helping us explain such models and create acquisitions more akin to human vision that only acquires the areas an operator needs, thereby reducing scan times.

Supervision history

Current supervision

Completed supervision

Enquiries

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