Shakes Chandra

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. 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

[2026] Charting a new melanoma risk factor: Satellite Naevus Prevalence in High-Risk and Population-Based Cohorts [2026] Generalised Medical Phrase Grounding [2026] Interpretable Semantic Medical Image Segmentation with Style and Confidence [2025] Prostate cancer radiation therapy shape variation analysis [2025] TripletResNet: a deep metric learning approach for mTBI diagnosis from 3D CT [2025] SAMRI [2025] A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers [2025] Identifying suspicious naevi with dermoscopy via variational autoencoder auxiliary generative classifiers [2025] Evaluating neural kernel surface reconstruction for extracting mesh geometry from pointclouds for volumetric analysis of sorghum panicles [2025] AEHRC at BioL