no code implementations • 27 Oct 2023 • Muhammad Bilal, Dinis Martinho, Reiner Sim, Adnan Qayyum, Hunaid Vohra, Massimo Caputo, Taofeek Akinosho, Sofiat Abioye, Zaheer Khan, Waleed Niaz, Junaid Qadir
This study introduces an end-to-end machine learning solution developed as part of our solution for the MICCAI 2023 Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs (ARCADE) challenge, which aims to benchmark solutions for multivessel coronary artery segmentation and potential stenotic lesion localisation from X-ray coronary angiograms.
Ranked #3 on Coronary Artery Segmentation on ARCADE