MedLesSynth-LD: Lesion synthesis using physics-based noise models for robust lesion segmentation in low-data medical imaging regimes
Narayanan R. and Sundaresan V., (2025), Pattern Recognition Letters, 188, 155 - 163
Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data.
Sundaresan V. et al, (2023)
Automated detection of cerebral microbleeds on MR images using knowledge distillation framework.
Sundaresan V. et al, (2023), Front Neuroinform, 17
Challenges for machine learning in clinical translation of big data imaging studies
Dinsdale NK. et al, (2022), Neuron, 110, 3866 - 3881
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge.
Campello VM. et al, (2021), IEEE Trans Med Imaging, 40, 3543 - 3554
Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images.
Sundaresan V. et al, (2021), Med Image Anal, 73
White matter hyperintensities classified according to intensity and spatial location reveal specific associations with cognitive performance.
Melazzini L. et al, (2021), NeuroImage. Clinical, 30
Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning.
Sundaresan V. et al, (2021), Front Neuroinform, 15
Automated Detection of Candidate Subjects with Cerebral Microbleeds using Machine Learning
Sundaresan V. et al, (2021)
Automated Detection of Cerebral Microbleeds on MR images using Knowledge Distillation Framework
Sundaresan V. et al, (2021)
Brain Tumour Segmentation Using a Triplanar Ensemble of U-Nets on MR Images
Sundaresan V. et al, (2021), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 340 - 353
Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images
Sundaresan V. et al, (2020)
Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference
Sundaresan V. et al, (2019), NeuroImage, 185, 434 - 445