bibtext: @InProceedings{ Ily_AHybrid_MICCAI2024,
                   author = { Ilyas, Zaid and Saleem, Afsah and Suter, David and Schousboe, John T. and Leslie, William D. and Lewis, Joshua R. and Gilani, Syed Zulqarnain },
                   title = { { A Hybrid CNN-Transformer Feature Pyramid Network for Granular Abdominal Aortic Calcification Detection from DXA Images } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15011 },
                   month = {October},
                   pages = { pending },
                }
abstract: Cardiovascular Diseases (CVDs) stand as the primary global cause of mortality, with Abdominal Aortic Calcification (AAC) being a stable marker of these conditions. AAC can be observed in Dual Energy X-ray absorptiometry (DXA) lateral view Vertebral Fracture Assessment (VFA) scans, usually performed for the detection of vertebral fractures. Early detection of AAC can help reduce the risk of developing clinical CVD by encouraging preventive measures. Recent efforts to automate DXA VFA image analysis for AAC detection are restricted to either predicting an overall AAC score, or they lack performance in granular AAC score prediction. The latter is important in helping clinicians predict CVD associated with the diminished Windkessel effect in the aorta. In this regard, we propose a hybrid Feature Pyramid Network (FPN) based CNN-Transformer architecture (Hybrid-FPN-AACNet) that employs a novel Dual Resolution Self-Attention (DRSA) mechanism to enhance context for self-attention by working on two different resolutions of the input feature map. Moreover, the proposed architecture also employs a novel Efficient Feature Fusion Module (EFFM) that efficiently combines the features from different hierarchies of Hybrid-FPN-AACNet for regression tasks. The proposed architecture has achieved State-Of-The-Art (SOTA) performance at a granular level compared to previous work.
title: A Hybrid CNN-Transformer Feature Pyramid Network for Granular Abdominal Aortic Calcification Detection from DXA Images
authors: Ilyas, Zaid Saleem, Afsah Suter, David Schousboe, John T. Leslie, William D. Lewis, Joshua R. Gilani, Syed Zulqarnain