  The accurate evaluation of left atrial fibrosis via high-quality 3D Late
Gadolinium Enhancement (LGE) MRI is crucial for atrial fibrillation management
but is hindered by factors like patient movement and imaging variability. The
pursuit of automated LGE MRI quality assessment is critical for enhancing
diagnostic accuracy, standardizing evaluations, and improving patient outcomes.
The deep learning models aimed at automating this process face significant
challenges due to the scarcity of expert annotations, high computational costs,
and the need to capture subtle diagnostic details in highly variable images.
This study introduces HAMIL-QA, a multiple instance learning (MIL) framework,
designed to overcome these obstacles. HAMIL-QA employs a hierarchical bag and
sub-bag structure that allows for targeted analysis within sub-bags and
aggregates insights at the volume level. This hierarchical MIL approach reduces
reliance on extensive annotations, lessens computational load, and ensures
clinically relevant quality predictions by focusing on diagnostically critical
image features. Our experiments show that HAMIL-QA surpasses existing MIL
methods and traditional supervised approaches in accuracy, AUROC, and F1-Score
on an LGE MRI scan dataset, demonstrating its potential as a scalable solution
for LGE MRI quality assessment automation. The code is available at:
$\href{https://github.com/arf111/HAMIL-QA}{\text{this https URL}}$
