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Great to have this paper online at #Nature #MachineIntelligence Nature Machine Intelligence!! Led by Himashi Himashi Peiris & Mehrtash Mehrtash. We show w as low as 10% labels great volumetric seg results can still be achieved! rigorous math framework is provided. nature.com/articles/s4225…

We are delighted to announce that our research, "Uncertainty-guided dual-views for semi-supervised volumetric medical image segmentation" has been published in Nature Machine Intelligence Nature Machine Intelligence. Check it out here: nature.com/articles/s4225…

AI can ask another AI for a second opinion on medical scans, research shows Nature Machine Intelligence doi.org/gsh39z techxplore.com/news/2023-07-a…

Our #Co-bioNet model Himashi Peiris for more reliable & label efficient tumour segmentation. -> Nat Mach Intell -> doi.org/10.1038/s42256…

#VBIC2023: Congratulations to VBIC ECR Award winner, Himashi Peiris for her work ‘Learning with limited labels for medical imaging analysis’

I am excited to share that I recently presented my PhD work at the Victorian Biomedical Imaging Capability Network meeting and was honored to receive the VBIC Early Career Researcher Award 2023. Big thanks to my supervisors for their unwavering support throughout this journey.

Really excited to see our large review piece out in Medical Image Analysis; we review medical image synthesis, analyse distinct network architectures, loss functions, quantify performance & provide overview of datasets. Challenges and opportunities are discussed. 👉