Machine Learning in Neuroimaging – Forum, October 31, 2025
https://malmic.ca/wp-content/uploads/2025/10/MRI-for-Brain-Tumor-Detection-scaled-1-1024x485.jpg 1024 485 MaLMIC - Machine Learning in Medical Imaging Consortium MaLMIC - Machine Learning in Medical Imaging Consortium https://malmic.ca/wp-content/uploads/2025/10/MRI-for-Brain-Tumor-Detection-scaled-1-1024x485.jpgJoin us for a high-impact session on AI for brain imaging that bridges cellular detail and clinical translation. Ahmadreza Attarpour’s talk will showcase ACE (Artificial intelligence-based Cartography of Ensembles) and MAPL3 (Mapping Axonal Projections in Light-sheet fluorescence microscopy in 3D): end-to-end deep-learning pipelines that turn teravoxel, cellular-resolution light-sheet datasets from cleared rodent brains into unbiased, brain-wide maps of local neuronal activity and connectivity—at both cellular and laminar scales. The second talk by Mahmoud Salman will shift to the neonatal clinic, introducing a robust deep-learning framework that overcomes poor contrast, motion artifacts, and tiny target structures to precisely segment hippocampal subfields and amygdala sub-nuclei from routine MRI. Together, these talks will demonstrate how scalable, generalizable methods are pushing neuroscience forward—from whole-brain mapping at unprecedented scale to reliable quantitative biomarkers of early brain maturation.
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