USC researchers publish an Alzheimer's model built for missing scans

Tamoghna Chattopadhyay and colleagues at the University of Southern California published MEMOIR-VLM on October 1st, a research model that classifies Alzheimer's disease from whatever combination of MRI, diffusion imaging and clinical scores is available, reaching 91.3% balanced accuracy distinguishing cognitively normal people from people with dementia on held-out research data and 68.2% accuracy across three categories including mild cognitive impairment. The paper's own results show cognitive test scores drove most of the diagnostic performance, and the language model did not beat nearest-neighbor retrieval.

USC researchers publish an Alzheimer's model built for missing scans MEMOIR-VLM combines MRI, diffusion imaging and clinical scores, but its strongest diagnostic signal came from cognitive tests. By Ryan Merket https://runtimewire.com/author/ryan-merket · Published Primary source: X https://x.com/TamoghnaChatto2/status/2107564670592913807 Why it matters MEMOIR-VLM tests whether one model can handle the incomplete scans common in practice. Its own results show the limit of the pitch: cognitive scores drive most diagnostic performance, and the language model did not beat nearest-neighbor retrieval. Tamoghna Chattopadhyay @TamoghnaChatto2 https://x.com/TamoghnaChatto2 and colleagues at the University of Southern California published MEMOIR-VLM on October 1st, a research model designed to classify Alzheimer's disease using whatever combination of brain scans and clinical data is available. The paper's headline result was 91.3% balanced accuracy distinguishing cognitively normal people from people with dementia on held-out research data. Its three-category accuracy, including mild cognitive impairment, was 68.2%.…