ArticlePredictive Intelligence in Medicine. PRIME (Workshop)2022
Multiple Instance Neuroimage Transformer.
Article in Predictive Intelligence in Medicine. PRIME (Workshop), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- When does more data help? Spectral Geometry and Scaling Laws in MRI Transformers.bioRxiv : the preprint server for biology · 2026Article
- A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data.Human brain mapping · 2024Article
- Article
- Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer's disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI.Frontiers in neuroscience · 2024Article
- Multi-modal deep learning and explainable AI for predicting multiple dementia-related neuropathologies from brain MRI, clinical, and genetic data.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
For the first time, we propose using a multiple instance learning based convolution-free transformer model, called Multiple Instance Neuroimage Transformer (MINiT), for the classification of T1-weighted (T1w) MRIs. We first present several variants of transformer models adopted for neuroimages. These models extract non-overlapping 3D blocks from the input volume and perform multi-headed self-attention on a sequence of their linear projections. MINiT, on the other hand, treats each of the non-overlapping 3D blocks of the input MRI as its own instance, splitting it further into non-overlapping 3D patches, on which multi-headed self-attention is computed. As a proof-of-concept, we evaluate the efficacy of our model by training it to identify sex from T1w-MRIs of two public datasets: Adolescent Brain Cognitive Development (ABCD) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA). The learned attention maps highlight voxels contributing to identifying sex differences in brain morphometry. The code is available at https://github.com/singlaayush/MINIT.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.