@article{kiricsli2013standardized,
  title={Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography},
  author={Kiri{\c{s}}li, HA and Schaap, Michiel and Metz, CT and Dharampal, AS and Meijboom, Willem Bob and Papadopoulou, Stella-Lida and Dedic, Admir and Nieman, Koen and de Graaf, Michiel A and Meijs, MFL and others},
  journal={Medical image analysis},
  volume={17},
  number={8},
  pages={859--876},
  year={2013},
  publisher={Elsevier}
}

@article{shahzad2013automatic,
  title={Automatic segmentation, detection and quantification of coronary artery stenoses on CTA},
  author={Shahzad, Rahil and Kiri{\c{s}}li, Hortense and Metz, Coert and Tang, Hui and Schaap, Michiel and van Vliet, Lucas and Niessen, Wiro and van Walsum, Theo},
  journal={The international journal of cardiovascular imaging},
  volume={29},
  pages={1847--1859},
  year={2013},
  publisher={Springer}
}

@article{vaswani2017attention,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@article{dosovitskiy2020image,
  title={An image is worth 16x16 words: Transformers for image recognition at scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and others},
  journal={arXiv preprint arXiv:2010.11929},
  year={2020}
}

@inproceedings{liu2021swin,
  title={Swin transformer: Hierarchical vision transformer using shifted windows},
  author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={10012--10022},
  year={2021}
}

@inproceedings{selvaraju2017grad,
  title={Grad-cam: Visual explanations from deep networks via gradient-based localization},
  author={Selvaraju, Ramprasaath R and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={618--626},
  year={2017}
}

@InProceedings{coronaryrcnn,
author="Zhang, Yu
and Ma, Jun
and Li, Jing",
editor="Wang, Linwei
and Dou, Qi
and Fletcher, P. Thomas
and Speidel, Stefanie
and Li, Shuo",
title="Coronary R-CNN: Vessel-Wise Method for Coronary Artery Lesion Detection and Analysis in Coronary CT Angiography",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022",
year="2022",
publisher="Springer Nature Switzerland",
address="Cham",
pages="207--216",
isbn="978-3-031-16437-8"
}

@inproceedings{diaz2019soft,
  title={Soft labels for ordinal regression},
  author={Diaz, Raul and Marathe, Amit},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={4738--4747},
  year={2019}
}

@article{naghavi2003vulnerable,
  title={From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: Part I},
  author={Naghavi, Morteza and Libby, Peter and Falk, Erling and Casscells, S Ward and Litovsky, Silvio and Rumberger, John and Badimon, Juan Jose and Stefanadis, Christodoulos and Moreno, Pedro and Pasterkamp, Gerard and others},
  journal={Circulation},
  volume={108},
  number={14},
  pages={1664--1672},
  year={2003},
  publisher={Am Heart Assoc}
}

@inproceedings{jensen2020ischemic,
  title={Ischemic heart disease: an update},
  author={Jensen, Rebekka Vibjerg and Hjortbak, Marie Vognstoft and B{\o}tker, Hans Erik},
  booktitle={Seminars in Nuclear Medicine},
  volume={50},
  number={3},
  pages={195--207},
  year={2020},
  organization={Elsevier}
}

@article{otsuka2013napkin,
  title={Napkin-ring sign on coronary CT angiography for the prediction of acute coronary syndrome},
  author={Otsuka, Kenichiro and Fukuda, Shota and Tanaka, Atsushi and Nakanishi, Koki and Taguchi, Haruyuki and Yoshikawa, Junichi and Shimada, Kenei and Yoshiyama, Minoru},
  journal={JACC: Cardiovascular Imaging},
  volume={6},
  number={4},
  pages={448--457},
  year={2013},
  publisher={American College of Cardiology Foundation Washington, DC}
}

@inproceedings{kelm2011detection,
  title={Detection, grading and classification of coronary stenoses in computed tomography angiography},
  author={Kelm, B Michael and Mittal, Sushil and Zheng, Yefeng and Tsymbal, Alexey and Bernhardt, Dominik and Vega-Higuera, Fernando and Zhou, S Kevin and Meer, Peter and Comaniciu, Dorin},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={25--32},
  year={2011},
  organization={Springer}
}

@article{zreik2018recurrent,
  title={A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography},
  author={Zreik, Majd and Van Hamersvelt, Robbert W and Wolterink, Jelmer M and Leiner, Tim and Viergever, Max A and I{\v{s}}gum, Ivana},
  journal={IEEE transactions on medical imaging},
  volume={38},
  number={7},
  pages={1588--1598},
  year={2018},
  publisher={IEEE}
}

@inproceedings{ma2021transformer,
  title={Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries},
  author={Ma, Xinghua and Luo, Gongning and Wang, Wei and Wang, Kuanquan},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={516--525},
  year={2021},
  organization={Springer}
}

@inproceedings{denzinger2019coronary,
  title={Coronary artery plaque characterization from CCTA scans using deep learning and radiomics},
  author={Denzinger, Felix and Wels, Michael and Ravikumar, Nishant and Breininger, Katharina and Reidelsh{\"o}fer, Anika and Eckert, Joachim and S{\"u}hling, Michael and Schmermund, Axel and Maier, Andreas},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={593--601},
  year={2019},
  organization={Springer}
}

@inproceedings{tejero2019texture,
  title={Texture-based classification of significant stenosis in CCTA multi-view images of coronary arteries},
  author={Tejero-de-Pablos, Antonio and Huang, Kaikai and Yamane, Hiroaki and Kurose, Yusuke and Mukuta, Yusuke and Iho, Junichi and Tokunaga, Youji and Horie, Makoto and Nishizawa, Keisuke and Hayashi, Yusaku and others},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={732--740},
  year={2019},
  organization={Springer}
}

@article{candemir2020automated,
  title={Automated coronary artery atherosclerosis detection and weakly supervised localization on coronary CT angiography with a deep 3-dimensional convolutional neural network},
  author={Candemir, Sema and White, Richard D and Demirer, Mutlu and Gupta, Vikash and Bigelow, Matthew T and Prevedello, Luciano M and Erdal, Barbaros S},
  journal={Computerized Medical Imaging and Graphics},
  volume={83},
  pages={101721},
  year={2020},
  publisher={Elsevier}
}

@article{cury2022cad,
  title={CAD-RADS 2.0--2022 Coronary Artery Disease-Reporting and Data System: An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR), and the North America Society of Cardiovascular Imaging (NASCI)},
  author={Cury, Ricardo C and Leipsic, Jonathon and Abbara, Suhny and Achenbach, Stephan and Berman, Daniel and Bittencourt, Marcio and Budoff, Matthew and Chinnaiyan, Kavitha and Choi, Andrew D and Ghoshhajra, Brian and others},
  journal={Cardiovascular Imaging},
  volume={15},
  number={11},
  pages={1974--2001},
  year={2022},
  publisher={American College of Cardiology Foundation Washington DC}
}

@inproceedings{yu2017volumetric,
  title={Volumetric ConvNets with mixed residual connections for automated prostate segmentation from 3D MR images},
  author={Yu, Lequan and Yang, Xin and Chen, Hao and Qin, Jing and Heng, Pheng Ann},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={31},
  number={1},
  year={2017}
}

@article{loshchilov2017decoupled,
  title={Decoupled weight decay regularization},
  author={Loshchilov, Ilya and Hutter, Frank},
  journal={arXiv preprint arXiv:1711.05101},
  year={2017}
}

@article{warrens2012some,
  title={Some paradoxical results for the quadratically weighted kappa},
  author={Warrens, Matthijs J},
  journal={Psychometrika},
  volume={77},
  pages={315--323},
  year={2012},
  publisher={Springer}
}

@inproceedings{DBLP:conf/aaai/MaFZL0WQ0025,
  author       = {Xinghua Ma and
                  Xinyan Fang and
                  Mingye Zou and
                  Gongning Luo and
                  Wei Wang and
                  Kuanquan Wang and
                  Zhaowen Qiu and
                  Xin Gao and
                  Shuo Li},
  editor       = {Toby Walsh and
                  Julie Shah and
                  Zico Kolter},
  title        = {A Trusted Lesion-assessment Network for Interpretable Diagnosis of
                  Coronary Artery Disease in Coronary {CT} Angiography},
  booktitle    = {AAAI-25, Sponsored by the Association for the Advancement of Artificial
                  Intelligence, February 25 - March 4, 2025, Philadelphia, PA, {USA}},
  pages        = {6009--6017},
  publisher    = {{AAAI} Press},
  year         = {2025},
  url          = {https://doi.org/10.1609/aaai.v39i6.32642},
  doi          = {10.1609/AAAI.V39I6.32642},
  timestamp    = {Sat, 31 May 2025 23:08:24 +0200},
  biburl       = {https://dblp.org/rec/conf/aaai/MaFZL0WQ0025.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/miccai/MaZFLLWWQGL24,
  author       = {Xinghua Ma and
                  Mingye Zou and
                  Xinyan Fang and
                  Yang Liu and
                  Gongning Luo and
                  Wei Wang and
                  Kuanquan Wang and
                  Zhaowen Qiu and
                  Xin Gao and
                  Shuo Li},
  editor       = {Marius George Linguraru and
                  Qi Dou and
                  Aasa Feragen and
                  Stamatia Giannarou and
                  Ben Glocker and
                  Karim Lekadir and
                  Julia A. Schnabel},
  title        = {Spatio-Temporal Contrast Network for Data-Efficient Learning of Coronary
                  Artery Disease in Coronary {CT} Angiography},
  booktitle    = {Medical Image Computing and Computer Assisted Intervention - {MICCAI}
                  2024 - 27th International Conference, Marrakesh, Morocco, October
                  6-10, 2024, Proceedings, Part {XI}},
  series       = {Lecture Notes in Computer Science},
  volume       = {15011},
  pages        = {645--655},
  publisher    = {Springer},
  year         = {2024},
  url          = {https://doi.org/10.1007/978-3-031-72120-5\_60},
  doi          = {10.1007/978-3-031-72120-5\_60},
  timestamp    = {Mon, 03 Mar 2025 21:18:00 +0100},
  biburl       = {https://dblp.org/rec/conf/miccai/MaZFLLWWQGL24.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{csr,
author = {Galdran, Adrian and Dolz, Jose and Chakor, Hadi and Lombaert, Herv\'{e} and Ben Ayed, Ismail},
title = {Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images},
year = {2020},
isbn = {978-3-030-59721-4},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-030-59722-1_64},
doi = {10.1007/978-3-030-59722-1_64},
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020: 23rd International Conference, Lima, Peru, October 4--8, 2020, Proceedings, Part V},
pages = {665--674},
numpages = {10},
keywords = {Cost-sensitive classifiers, Diabetic retinopathy grading},
location = {Lima, Peru}
}

@INPROCEEDINGS{ordinal,
  author={Niu, Zhenxing and Zhou, Mo and Wang, Le and Gao, Xinbo and Hua, Gang},
  booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  title={Ordinal Regression with Multiple Output CNN for Age Estimation},
  year={2016},
  volume={},
  number={},
  pages={4920-4928},
  keywords={Estimation;Face;Feature extraction;Aging;Support vector machines;Training data;Measurement},
  doi={10.1109/CVPR.2016.532}}

@INPROCEEDINGS{scpr,
  author={Kanitsar, A. and Fleischmann, D. and Wegenkittl, R. and Felkel, P. and Groller, E.},
  booktitle={IEEE Visualization, 2002. VIS 2002.},
  title={CPR - curved planar reformation},
  year={2002},
  volume={},
  number={},
  pages={37-44},
  keywords={Biomedical imaging;Computed tomography;Data visualization;Magnetic resonance imaging;Blood vessels;Image generation;Computer graphics;Radiology;Computer displays;Chromium},
  doi={10.1109/VISUAL.2002.1183754}}

@INPROCEEDINGS{10635120,
  author={Hou, Junjie and Liao, Nianxi and Liu, Jia and Liu, Yuhang and Qiu, Jianxing},
  booktitle={2024 IEEE International Symposium on Biomedical Imaging (ISBI)},
  title={Learning Disentangled Representation for Vessel-Specific Coronary Artery Calcium Scoring},
  year={2024},
  volume={},
  number={},
  pages={1-5},
  keywords={Measurement;Image segmentation;Graphical models;Calcium;Computed tomography;Prediction algorithms;Cardiovascular diseases;Vessel-specific calcium scoring;Disentangled representation learning;Domain knowledge},
  doi={10.1109/ISBI56570.2024.10635120}}

@INPROCEEDINGS {11094417,
author = { Zhao, Ziwei and Zhang, Zhixing and Liu, Yuhang and Zhang, Zhao and Yu, Haojun and Wang, Dong and Wang, Liwei },
booktitle = { 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) },
title = {{ DeformCL: Learning Deformable Centerline Representation for Vessel Extraction in 3D Medical Image }},
year = {2025},
volume = {},
ISSN = {},
pages = {30896-30905},
abstract = { In the field of 3D medical imaging, accurately extracting and representing the blood vessels with curvilinear structures holds paramount importance for clinical diagnosis. Previous methods have commonly relied on discrete representation like mask, often resulting in local fractures or scattered fragments due to the inherent limitations of the per-pixel classification paradigm. In this work, we introduce DeformCL, a new continuous representation based on Deformable Centerlines, where centerline points act as nodes connected by edges that capture spatial relationships. Compared with previous representations, DeformCL offers three key advantages: natural connectivity, noise robustness, and interaction facility. We present a comprehensive training pipeline structured in a cascaded manner to fully exploit these favorable properties of DeformCL. Extensive experiments on four 3D vessel segmentation datasets demonstrate the effectiveness and superiority of our method. Furthermore, the visualization of curved planar reformation images validates the clinical significance of the proposed framework. We release the code in https://github.com/barry664/DeformCL. },
keywords = {Training;Visualization;Image segmentation;Three-dimensional displays;Deformation;Pipelines;Pattern recognition;Noise robustness;Clinical diagnosis;Medical diagnostic imaging},
doi = {10.1109/CVPR52734.2025.02877},
url = {https://doi.ieeecomputersociety.org/10.1109/CVPR52734.2025.02877},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month =Jun}

@inproceedings{10.1007/978-3-031-43990-2_71,
	abstract = {Automatic labeling of coronary arteries is an essential task in the practical diagnosis process of cardiovascular diseases. For experienced radiologists, the anatomically predetermined connections are important for labeling the artery segments accurately, while this prior knowledge is barely explored in previous studies. In this paper, we present a new framework called TopoLab which incorporates the anatomical connections into the network design explicitly. Specifically, the strategies of intra-segment feature aggregation and inter-segment feature interaction are introduced for hierarchical segment feature extraction. Moreover, we propose the anatomy-aware connection classifier to enable classification for each connected segment pair, which effectively exploits the prior topology among the arteries with different categories. To validate the effectiveness of our method, we contribute high-quality annotations of artery labeling to the public orCaScore dataset. The experimental results on both the orCaScore dataset and an in-house dataset show that our TopoLab has achieved state-of-the-art performance.},
	address = {Cham},
	author = {Zhang, Zhixing and Zhao, Ziwei and Wang, Dong and Zhao, Shishuang and Liu, Yuhang and Liu, Jia and Wang, Liwei},
	booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023},
	editor = {Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood, Tanveer and Taylor, Russell},
	isbn = {978-3-031-43990-2},
	pages = {759--769},
	publisher = {Springer Nature Switzerland},
	title = {Topology-Preserving Automatic Labeling of Coronary Arteries via Anatomy-Aware Connection Classifier},
	year = {2023}}

@inproceedings{10.1007/978-3-031-19803-8_22,
author = {Wang, Dong and Zhang, Zhao and Zhao, Ziwei and Liu, Yuhang and Chen, Yihong and Wang, Liwei},
title = {PointScatter: Point Set Representation for Tubular Structure Extraction},
year = {2022},
isbn = {978-3-031-19802-1},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-19803-8_22},
doi = {10.1007/978-3-031-19803-8_22},
abstract = {This paper explores the point set representation for tubular structure extraction tasks. Compared with the traditional mask representation, the point set representation enjoys its flexibility and representation ability, which would not be restricted by the fixed grid as the mask. Inspired by this, we propose PointScatter, an alternative to the segmentation models for the tubular structure extraction task. PointScatter splits the image into scatter regions and parallelly predicts points for each scatter region. We further propose the greedy-based region-wise bipartite matching algorithm to train the network end-to-end and efficiently. We benchmark the PointScatter on four public tubular datasets, and the extensive experiments on tubular structure segmentation and centerline extraction task demonstrate the effectiveness of our approach.Code is available at.},
booktitle = {Computer Vision -- ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XXI},
pages = {366--383},
numpages = {18},
keywords = {Tubular structure, Medical image segmentation, Centerline extraction, Point set representation},
location = {Tel Aviv, Israel}
}

@article{mowatt200864,
  title={64-Slice computed tomography angiography in the diagnosis and assessment of coronary artery disease: systematic review and meta-analysis},
  author={Mowatt, Graham and Cook, Jonathan A and Hillis, Graham S and Walker, Shonagh and Fraser, Cynthia and Jia, Xueli and Waugh, Norman},
  journal={Heart},
  volume={94},
  number={11},
  pages={1386--1393},
  year={2008},
  publisher={BMJ Publishing Group Ltd}
}
