(2022) Hangfan’s new method called “ADCoC: Adaptive Distribution Modeling Based Collaborative Clustering for Disentangling Disease Heterogeneity from Neuroimaging Data” has been accepted for publication in IEEE Transactions on Emerging Topics in Computational Intelligence, congrats Hangfan for this landmark paper
(2021) Hangfan Liu has presented his work “Adaptive Squeeze-and-Shrink Image Denoising for Improving Deep Detection of Cerebral Microbleeds” in MICCAI 2021 in Strasbourg, France, congrats Hangfan for the great work
(2021) We had an excellent workshop “Machine learning in neuroimaging” at the MICCAI 2021, with great plenary talk from Paul Thomoson
(2021) Christina Dintica, PhD, a postdoctoral scholar in the UCSF Department of Psychiatry and member of the Kristine Yaffe Lab, has been awarded an Alzheimer's Association Research Fellowship! Under the mentorship of Dr. Yaffe and UT Health San Antonio's Dr. Mohamad Habes.
(2021) Tanweer’s paper DeepMIR detecting cerebral microbleeds and iron deposits using MRI, advanced quantitative methods and Deep Learning has been accepted in Scientific Reports https://rdcu.be/co79B. Congrats Tanweer Rashid for the excellent work.
(2021) We have received the San Antonio Medical Foundation (SAMF) grant in collaboration with UTSA (Dhireesha Kudithipudi) and the VA (Adetoun Musa) to apply artificial intelligence to MRI and EEG data and subtype dementia patterns.
(2020) We published our work "The Brain Chart of Aging MachineLearning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans" in Alzheimer’s and dementia, the journal of Alzheimer’s Association.
(2020) We had a great featured research session with people excited about AI and its emerging role in uncovering dementia at the virtual AAIC2020.
(2020) Tanweer Rashid presented his work on deep learning for cerebral microbleeds and iron deposits detection in MRI.
(2020) Our paper is now online in Biological Psychiatry ” Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods”, summarizing precision medicine approaches to subtyping Alzheimer's disease with advanced machine learning methods, applied on neuroimaging and other phenotyping data, for future care!
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