  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 T1weighted (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.
