bibtext: @InProceedings{ Sul_HAMILQA_MICCAI2024,
                   author = { Sultan, K. M. Arefeen and Hisham, Md Hasibul Husain and Orkild, Benjamin and Morris, Alan and Kholmovski, Eugene and Bieging, Erik and Kwan, Eugene and Ranjan, Ravi and DiBella, Ed and Elhabian, Shireen Y. },
                   title = { { HAMIL-QA: Hierarchical Approach to Multiple Instance Learning for Atrial LGE MRI Quality Assessment } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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: https://github.com/arf111/HAMIL-QA
title: HAMIL-QA: Hierarchical Approach to Multiple Instance Learning for Atrial LGE MRI Quality Assessment
authors: Sultan, K. M. Arefeen Hisham, Md Hasibul Husain Orkild, Benjamin Morris, Alan Kholmovski, Eugene Bieging, Erik Kwan, Eugene Ranjan, Ravi DiBella, Ed Elhabian, Shireen Y.