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Dr Shakes Chandra

Senior Lecturer

School of Information Technology and Electrical Engineering
Faculty of Engineering, Architecture and Information Technology
shekhar.chandra@uq.edu.au
+61 7 336 58359

Overview

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.

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.

Qualifications

  • Doctor of Philosophy, Monash University

Publications

View all Publications

Grants

View all Grants

Supervision

  • Deep Learning Applied to Medical Image Analysis

    Doctor Philosophy

  • Magnetic Resonance Image Processing with Artificial Intelligence

    Doctor Philosophy

  • Towards Efficient Graph Neural Networks for Optimizing Illicit Dark Web Interventions

    Doctor Philosophy

View all Supervision

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.

View all Available Projects

Publications

Journal Article

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

  • 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

  • FDGATII: Fast Dynamic Graph Attention with Initial Residual and Identity

    Kulatilleke, Gayan K., Portmann, Marius, Ko, Ryan and Chandra, Shekhar S. (2022). FDGATII: Fast Dynamic Graph Attention with Initial Residual and Identity. 35th Australasian Joint Conference on Artificial Intelligence: AI 2022, Perth, WA Australia, 5–8 December 2022. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-22695-3_6

  • Undersampled MRI reconstruction with side information-guided normalisation

    Liu, Xinwen, Wang, Jing, Peng, Cheng, Chandra, Shekhar S., Liu, Feng and Zhou, S. Kevin (2022). Undersampled MRI reconstruction with side information-guided normalisation. Medical Image Computing and Computer Assisted Intervention – MICCAI, Singapore, Singapore, 18-22 September 2022. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-16446-0_31

  • Slim-YOLO: a simplified object detection model for the detection of pigmented iris freckles as a potential biomarker for cutaneous melanoma

    Naranpanawa, D. Nathasha U., Gu, Yanyang, Chandra, Shekhar S., Betz-Stablein, Brigid, Sturm, Richard A., Soyer, H. Peter and Eriksson, Anders P. (2021). Slim-YOLO: a simplified object detection model for the detection of pigmented iris freckles as a potential biomarker for cutaneous melanoma. Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, Australia, 29 November - 1 December 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/dicta52665.2021.9647150

  • Can3d: Fast 3D knee mri segmentation via compact context aggregation

    Dai, Wei, Woo, Boyeong, Liu, Siyu, Marques, Matthew, Tang, Fangfang, Crozier, Stuart, Engstrom, Craig and Chandra, Shekhar (2021). Can3d: Fast 3D knee mri segmentation via compact context aggregation. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France, 13-16 April 2021. Piscataway, NJ United States: IEEE. doi: 10.1109/isbi48211.2021.9433784

  • End-to-end ugly duckling sign detection for melanoma identification with transformers

    Yu, Zhen, Mar, Victoria, Eriksson, Anders, Chandra, Shakes, Bonnington, Paul, Zhang, Lei and Ge, Zongyuan (2021). End-to-end ugly duckling sign detection for melanoma identification with transformers. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, Strasbourg, France, 27 September-1 October 2021. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-030-87234-2_17

  • Deep simultaneous optimization of sampling and reconstruction for multi-contrast MRI

    Liu, Xinwen, Wang, Jing, Tang, Fangfang, Chandra, Shekhar S., Liu, Feng and Crozier, Stuart (2020). Deep simultaneous optimization of sampling and reconstruction for multi-contrast MRI. ISMRM & SMRT Virtual Conference & Exhibition, 2020, Online, 8-14 August 2020.

  • Fast high dynamic range MRI by Contrast Enhancement Networks

    Marques, Matthew, Engstrom, Craig, Fripp, Jurgen, Crozier, Stuart and Chandra, Shekhar S. (2020). Fast high dynamic range MRI by Contrast Enhancement Networks. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, United States, 3-7 April 2020. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/isbi45749.2020.9098373

  • Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN

    Min, Hang, Wilson, Devin, Huang, Yinhuang, Liu, Siyu, Crozier, Stuart, Bradley, Andrew P. and Chandra, Shekhar S. (2020). Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN. 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, United States, 3-7 April 2020. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/isbi45749.2020.9098732

  • SPIFFY: a simpler image viewer for medical imaging

    Sun, Jiayu and Chandra, Shekhar S. (2018). SPIFFY: a simpler image viewer for medical imaging. 4th Information Technology and Mechatronics Engineering Conference (ITOEC2018), Chongqing, China, 14-16 December 2018. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/ITOEC.2018.8740656

  • Multi-scale mass segmentation for mammograms via cascaded random forests

    Min, Hang, Chandra, Shekhar S., Dhungel, Neeraj, Crozier, Stuart and Bradley, Andrew P. (2017). Multi-scale mass segmentation for mammograms via cascaded random forests. 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017, Melbourne, VIC, Australia, 18 - 21 April 2017. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ISBI.2017.7950481

  • Automated intervertebral disc segmentation using probabilistic shape estimation and active shape models

    Neubert, Aleš, Fripp, Jurgen, Chandra, Shekhar S., Engstrom, Craig and Crozier, Stuart (2016). Automated intervertebral disc segmentation using probabilistic shape estimation and active shape models. Third International Workshop and Challenge, CSI 2015, Munich, Germany, 5 October 2015. Switzerland: Springer. doi: 10.1007/978-3-319-41827-8_15

  • Finite radial reconstruction for magnetic resonance imaging: a theoretical study

    Chandra, Shekhar S., Archchige, Ramitha, Ruben, Gary, Jin, Jin, Li, Mingyan, Kingston, Andrew M., Svalbe, Imants and Crozier, Stuart (2016). Finite radial reconstruction for magnetic resonance imaging: a theoretical study. International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016, Gold Coast, QLD, Australia, 30 November-2 December 2016. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/DICTA.2016.7797043

  • Incremental shape learning of 3D surfaces of the knee, data from the osteoarthritis initiative

    Neubert, Ales, Naser, Ibrahim, Paproki, Anthony, Engstrom, Craig, Fripp, Jurgen, Crozier, Stuart and Chandra, Shekhar S. (2016). Incremental shape learning of 3D surfaces of the knee, data from the osteoarthritis initiative. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic, 13-16 April, 2016. Piscataway, United States: IEEE Operations Center. doi: 10.1109/ISBI.2016.7493406

  • Shape reconstruction using EM-ICP mesh registration and robust statistical shape models

    Neubert, Ales, Chandra, Shekhar S., Engstrom, Craig, Fripp, Jurgen and Crozier, Stuart (2015). Shape reconstruction using EM-ICP mesh registration and robust statistical shape models. Symposium on Statistical Shape Models Applications, Delemont, Switzerland, 30 September - 2 October, 2015.

  • Automatic Bone Segmentation for Shoulder {MRI} using Statistical Shape Models

    Yang, Zhengyi, Fripp, Jurgen, Engstrom, Craig, Chandra, Shekhar, Xia, Ying, Paproki, Anthony, Strudwick, Mark, Neubert, Ales and Crozier, Stuart (2014). Automatic Bone Segmentation for Shoulder {MRI} using Statistical Shape Models. 2014 – Joint Annual Meeting ISMRM-ESMRMB, 22nd Scientific Meeting and Exhibition, Milan, Italy, 10-16 May 2014. Berkeley, CA United States: International Society for Magnetic Resonance in Medicine.

  • Automatic atlas based electron density and structure contouring for MRI-based prostate radiation therapy on the cloud

    Dowling, J. A., Burdett, N., Greer, P. B., Sun, J., Parker, J., Pichler, P., Stanwell, P., Chandra, S., Rivest-Henault, D., Ghose, S., Salvado, O. and Fripp, J. (2014). Automatic atlas based electron density and structure contouring for MRI-based prostate radiation therapy on the cloud. XVII International Conference on the Use of Computers in Radiation Therapy, Melbourne, Australia, 6–9 May 2013. Bristol, United Kingdom: Institute of Physics Publishing. doi: 10.1088/1742-6596/489/1/012048

  • Fast cine-magnetic resonance imaging point tracking for prostate cancer radiation therapy planning

    Dowling, Jason, Dang, K., Fox, Chris D., Chandra, S., Gill, Suki, Kron, T., D Pham, D. and Foroudi, F. (2014). Fast cine-magnetic resonance imaging point tracking for prostate cancer radiation therapy planning. ICCR 2013: XVII International Conference on the Use of Computers in Radiation Therapy, Melbourme, VIC, Australia, 6–9 May, 2013. Temple Way, Bristol, United Kingdom: Institute of Physics Publishing. doi: 10.1088/1742-6596/489/1/012027

  • Direct Inversion of Mojette Projections

    Svalbe, Imants, Kingston, Andrew, Guedon, Jeanpierre, Normand, Nicolas and Chandra, Shekhar S. (2013). Direct Inversion of Mojette Projections. 20th IEEE International Conference on Image Processing, ICIP 2013, Melbourne , Australia, 15 - 18 September 2013. Piscataway, NJ United States: IEEE. doi: 10.1109/ICIP.2013.6738214

  • Automated bone segmentation and bone-cartilage interface extraction from MR images of the hip

    Xia, Ying, Chandra, Shakes, Salvado, Oliver, Fripp, Jurgen, Schwartz, Raphael, Lauer, Lars, Engstrom, Craig M. and Crozier, Stuart (2012). Automated bone segmentation and bone-cartilage interface extraction from MR images of the hip. International Society for Magnetic Resonance in Medicine, Melbourme, VIC, Australia, 5-11 May 2012.

  • Morphology-based interslice interpolation on manual segmentations of joint bones and muscles in MRI

    Yang, Zhengyi, Crozier, Stuart, Engstrom, Craig, Xia, Ying, Neubert, Ales, Brancato, Tania, Schwarz, Raphael, Lauer, Lars, Fripp, Jurgen, Chandra, Shekhar and Salvado, Olivier (2012). Morphology-based interslice interpolation on manual segmentations of joint bones and muscles in MRI. 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA), Fremantle, WA, Australia, 3-5 December 2012. Piscataway, NJ, United States: IEEE. doi: 10.1109/DICTA.2012.6411678

  • Unilateral hip joint segmentation with shape priors learned from missing data

    Chandra, Shekhar, Xia, Yinq, Engstrom, Craig, Schwarz, Raphael, Lauer, Lars, Crozier, Stuart, Salvado, Olivier and Fripp, Jurgen (2012). Unilateral hip joint segmentation with shape priors learned from missing data. 9th IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, Spain, 2-5 March 2012. Piscataway, NJ, United States: IEEE. doi: 10.1109/ISBI.2012.6235909

  • Automated MR hip bone segmentation

    Xia, Ying, Chandra, Shakes, Salvado, Olivier, Fripp, Jurgen, Schwarz, Raphael, Lauer, Lars, Engstrom, Craig and Crozier, Stuart (2011). Automated MR hip bone segmentation. International Conference on Digital Image Computing Techniques and Applications (DICTA), Noosa, QLD, Australia, 6-8 December 2011. Piscataway, NJ, United States: IEEE. doi: 10.1109/DICTA.2011.13

  • Automatic segmentation of the prostate in 3D magnetic resonance images using case specific deformable models

    Chandra, Shekhar, Dowling, Jason, Shen, Kaikai, Pluim, Josien, Greer, Peter, Salvado, Olivier and Fripp, Jurgen (2011). Automatic segmentation of the prostate in 3D magnetic resonance images using case specific deformable models. International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011, Noosa Heads, Qld., Australia, 6-8 December 2011. Piscataway, NJ, United States: I E E E. doi: 10.1109/DICTA.2011.10

  • Fast automatic multi-atlas segmentation of the prostate from 3D MR images

    Dowling, Jason A., Fripp, Jurgen, Chandra, Shekhar, Pluim, Josien P. W., Lambert, Jonathan, Parker, Joel, Denham, James, Greer, Peter B. and Salvado, Olivier (2011). Fast automatic multi-atlas segmentation of the prostate from 3D MR images. 14th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011), Toronto, Canada, 18-22 September 2011. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-23944-1_2

  • Growth of Discrete Projection Ghosts Created by Iteration

    Svalbe, Imants and Chandra, Shekhar (2011). Growth of Discrete Projection Ghosts Created by Iteration. 16th International Conference on Discrete Geometry for Computer Imagery, Nancy France, 6 - 8 April 2011. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-19867-0_34

  • On constructing minimal ghosts

    Svalbe, Imants, Nazareth, Nikesh, Chandra, Shekhar and Normand, Nicolas (2010). On constructing minimal ghosts. International Conference on Digital Image Computing: Techniques and Applications, DICTA 2010, Sydney, NSW Australia, 01 - 03 December 2010. Piscataway, NJ United States: I E E E. doi: 10.1109/DICTA.2010.56

  • A fast number theoretic finite radon transform

    Chandra, S. and Svalbe, I. (2009). A fast number theoretic finite radon transform. Digital Image Computing: Techniques and Applications, DICTA 2009, Melbourne, VIC Australia, 1 - 3 December 2009. Piscataway, NJ United States: I E E E. doi: 10.1109/DICTA.2009.67

  • A method for removing cyclic artefacts in discrete tomography using latin squares

    Chandra, Shekhar and Svalbe, Imants (2008). A method for removing cyclic artefacts in discrete tomography using latin squares. 19th International Conference on Pattern Recognition (ICPR 2008), Tampa, Fl United States, 8 - 11 Dec 2008. Washington, DC United States: I E E E Computer Society. doi: 10.1109/ICPR.2008.4761615

  • An exact, non-iterative Mojette inversion technique utilising ghosts

    Chandra, Shekhar, Svalbe, Imants and Guedon, Jean-Pierre (2008). An exact, non-iterative Mojette inversion technique utilising ghosts. 14th International Conference on Discrete Geometry for Computer Imagery, Lyon, France, 16 - 18 April 2008. Heidelberg, Germany: Springer. doi: 10.1007/978-3-540-79126-3_36

  • Quantised angular momentum vectors and projection angle distributions for discrete radon transformations

    Svalbe, Imants, Chandra, Shekhar, Kingston, Andrew and Guédon, Jean-Pierre (2006). Quantised angular momentum vectors and projection angle distributions for discrete radon transformations. 13th International Conference on Discrete Geometry for Computer Imagery, DGCI 2006, , , October 25, 2006-October 27, 2006. Springer Verlag. doi: 10.1007/11907350_12

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

  • Circulant theory of the Radon transform

    Chandra, Shekhar Suresh (2010). Circulant theory of the Radon transform. PhD Thesis, Faculty of Science, School of Physics, Monash University.

PhD and MPhil Supervision

Current Supervision

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • 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.

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Links

ResearcherID: B-3698-2011

ORCID: 0000-0001-6544-900X

Scopus ID: 15130876000

Google Scholar ID: tKe_olQAAAAJ

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