Stenosis Detection in X-ray Coronary Angiography with Deep Neural Networks Leveraged by Attention Mechanisms

Author:

Stralen Pedro Van1ORCID,Rodrigues Dinis L.1ORCID,Oliveira Arlindo L.1ORCID,Menezes Miguel N.1ORCID,Pinto Fausto J.1ORCID

Affiliation:

1. Instituto Superior Técnico, Instituto Superior Técnico, Portugal

Publisher

ACM

Reference44 articles.

1. Automatic stenosis recognition from coronary angiography using convolutional neural networks;Moon J.H.;Computer Methods and Programs in Biomedicine,2020

2. Tan, M.; Pang, R.; Le, Q.V. EfficientDet : Scalable and efficient object detection . CVPR 2020 Tan, M.; Pang, R.; Le, Q.V. EfficientDet: Scalable and efficient object detection. CVPR 2020

3. Seung-Wook Kim , Hyong-Keun Kook , Jee-Young Sun , Mun-Cheon Kang , and Sung-Jea Ko . Parallel feature pyramid network for object detection . ECCV , 2018 Seung-Wook Kim, Hyong-Keun Kook, Jee-Young Sun, Mun-Cheon Kang, and Sung-Jea Ko. Parallel feature pyramid network for object detection. ECCV, 2018

4. Lin, T., Goyal, P., Girshick, R., He, K., Dollar, P. : Focal loss for dense object detection . ICCV , 2017 Lin, T., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. ICCV, 2017

5. NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

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1. Exploiting Pre-trained Architectures for Dual-Stream Classification of LCA-RCA in a Private AngioData;2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA);2023-09-20

2. Convolutional neural networks on risk stratification of patients with suspected coronary artery disease undergoing coronary computed tomography angiography;The International Journal of Cardiovascular Imaging;2023-04-03

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