@article{covidCTdataset,
  title={Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation},
  author={Ma, Jun and Wang, Yixin and An, Xingle and Ge, Cheng and Yu, Ziqi and Chen, Jianan and Zhu, Qiongjie and Dong, Guoqiang and He, Jian and He, Zhiqiang and others},
  journal={Medical physics},
  volume={48},
  number={3},
  pages={1197--1210},
  year={2021},
  publisher={Wiley Online Library}
}

@book{chestCTsurvey,
  title={ExpertDDx: Chest},
  author={Carter, Brett W and Rosado-de-Christenson, Melissa L and Lichtenberger III, John P and Mart{\'\i}nez-Jim{\'e}nez, Santiago},
  year={2020},
  publisher={Elsevier Health Sciences}
}

@article{khanna2020automated,
  title={Automated Localized Approach for Airway Segmentation in 3D Chest CT Volume},
  author={Khanna, Anita and Londhe, Narendra Digambar and Gupta, Shubhrata},
  journal={Biomedical and Pharmacology Journal},
  volume={13},
  number={4},
  pages={1671--1682},
  year={2020}
}

@article{milam2023current,
  title={The current status and future of FDA-approved artificial intelligence tools in chest radiology in the United States},
  author={Milam, ME and Koo, CW},
  journal={Clinical Radiology},
  volume={78},
  number={2},
  pages={115--122},
  year={2023},
  publisher={Elsevier}
}

@article{rajpurkar2022ai,
  title={AI in health and medicine},
  author={Rajpurkar, Pranav and Chen, Emma and Banerjee, Oishi and Topol, Eric J},
  journal={Nature medicine},
  volume={28},
  number={1},
  pages={31--38},
  year={2022},
  publisher={Nature Publishing Group US New York}
}

@article{yang2024limits,
  title={The limits of fair medical imaging AI in real-world generalization},
  author={Yang, Yuzhe and Zhang, Haoran and Gichoya, Judy W and Katabi, Dina and Ghassemi, Marzyeh},
  journal={Nature Medicine},
  volume={30},
  number={10},
  pages={2838},
  year={2024}
}

@inproceedings{AdamW,
  title={Decoupled Weight Decay Regularization},
  author={Ilya Loshchilov and Frank Hutter},
  booktitle={International Conference on Learning Representations},
  year={2017},
  url={https://api.semanticscholar.org/CorpusID:53592270}
}

@article{radgenome,
  title={RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis},
  author={Zhang, Xiaoman and Wu, Chaoyi and Zhao, Ziheng and Lei, Jiayu and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
  journal={arXiv preprint arXiv:2404.16754},
  year={2024}
}

@ARTICLE{10540605,
  author={Liu, Mingxuan and Jiao, Yunrui and Lu, Jingqiao and Chen, Hong},
  journal={IEEE Transactions on Instrumentation and Measurement}, 
  title={Anomaly Detection for Medical Images Using Teacher–Student Model With Skip Connections and Multiscale Anomaly Consistency}, 
  year={2024},
  volume={73},
  number={},
  pages={1-15},
  keywords={Feature extraction;Anomaly detection;Training;Image reconstruction;Data models;Medical diagnostic imaging;Location awareness;Anomaly detection (AD);deep learning;knowledge distillation (KD);medical image analysis;teacher-student (T-S) model},
  doi={10.1109/TIM.2024.3406792}}

@inproceedings{liu2023skip,
  title={Skip-st: Anomaly detection for medical images using student-teacher network with skip connections},
  author={Liu, Mingxuan and Jiao, Yunrui and Chen, Hong},
  booktitle={2023 IEEE International Symposium on Circuits and Systems (ISCAS)},
  pages={1--5},
  year={2023},
  organization={IEEE}
}

@article{cao2022informative,
  title={Informative knowledge distillation for image anomaly segmentation},
  author={Cao, Yunkang and Wan, Qian and Shen, Weiming and Gao, Liang},
  journal={Knowledge-Based Systems},
  volume={248},
  pages={108846},
  year={2022},
  publisher={Elsevier}
}

@ARTICLE{10402554,
  author={Cao, Yunkang and Xu, Xiaohao and Sun, Chen and Gao, Liang and Shen, Weiming},
  journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems}, 
  title={BiaS: Incorporating Biased Knowledge to Boost Unsupervised Image Anomaly Localization}, 
  year={2024},
  volume={54},
  number={4},
  pages={2342-2353},
  keywords={Location awareness;Task analysis;Knowledge engineering;Inspection;Training;Testing;Anomaly localization;disentangle learning;few-shot learning;knowledge distillation},
  doi={10.1109/TSMC.2023.3344383}}

@INPROCEEDINGS{9746715,
  author={Xu, Rui and Wang, Yufeng and Ye, Xinchen and Wu, Pengcheng and Chen, Yen-Wei and Xu, Fangyi and Zhu, Wenchao and Chen, Chao and Zhou, Yong and Hu, Hongjie and Qu, Xiaofeng and Kido, Shoji and Tomiyama, Noriyuki},
  booktitle={ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Pixel-Level and Affinity-Level Knowledge Distillation for Unsupervised Segmentation of Covid-19 Lesions}, 
  year={2022},
  volume={},
  number={},
  pages={1376-1380},
  keywords={COVID-19;Knowledge engineering;Training;Image segmentation;Computed tomography;Signal processing;Lesions;Knowledge Distillation;Unsupervised Segmentation;Anomaly Detection;Affinity-Level},
  doi={10.1109/ICASSP43922.2022.9746715}}

@article{bercea2024towards,
  title={Towards Universal Unsupervised Anomaly Detection in Medical Imaging},
  author={Bercea, Cosmin I and Wiestler, Benedikt and Rueckert, Daniel and Schnabel, Julia A},
  journal={arXiv preprint arXiv:2401.10637},
  year={2024}
}

@article{bergomi2024reshaping,
  title={Reshaping Free-Text Radiology Notes Into Structured Reports With Generative Transformers},
  author={Bergomi, Laura and Buonocore, Tommaso M and Antonazzo, Paolo and Alberghi, Lorenzo and Bellazzi, Riccardo and Preda, Lorenzo and Bortolotto, Chandra and Parimbelli, Enea},
  journal={arXiv preprint arXiv:2403.18938},
  year={2024}
}

@inproceedings{radford2021learning,
  title={Learning transferable visual models from natural language supervision},
  author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
  booktitle={International conference on machine learning},
  pages={8748--8763},
  year={2021},
  organization={PMLR}
}

@article{ct-clip,
  author    = {Ibrahim Ethem Hamamci and Sezgin Er and Furkan Almas and others},
  title     = {Developing Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography},
  year      = {2024},
  journal   = {PREPRINT (Version 1) available at Research Square},
  url       = {https://doi.org/10.21203/rs.3.rs-5271327/v1},
  doi       = {10.21203/rs.3.rs-5271327/v1},
  month     = {October},
  day       = {27}
}

@article{report_method1,
  title={Classification of multiple diseases on body CT scans using weakly supervised deep learning},
  author={Tushar, Fakrul Islam and D’Anniballe, Vincent M and Hou, Rui and Mazurowski, Maciej A and Fu, Wanyi and Samei, Ehsan and Rubin, Geoffrey D and Lo, Joseph Y},
  journal={Radiology: Artificial Intelligence},
  volume={4},
  number={1},
  pages={e210026},
  year={2021},
  publisher={Radiological Society of North America}
}

@INPROCEEDINGS{10222528,
  author={Huang, Yiming and Liu, Guole and Luo, Yaoru and Yang, Ge},
  booktitle={2023 IEEE International Conference on Image Processing (ICIP)}, 
  title={ADFA: Attention-Augmented Differentiable Top-K Feature Adaptation for Unsupervised Medical Anomaly Detection}, 
  year={2023},
  volume={},
  number={},
  pages={206-210},
  keywords={Location awareness;Image analysis;Training data;Feature extraction;Lesions;Anomaly detection;Biomedical imaging;anomaly detection;medical image;unsupervised learning;attention mechanism;feature adaptation},
  doi={10.1109/ICIP49359.2023.10222528}}

@inproceedings{zhou2023spatial,
  author    = {Yanfeng Zhou and Yiming Huang and Ge Yang},
  title     = {Spatial and Planar Consistency for Semi-Supervised Volumetric Medical Image Segmentation},
  booktitle = {Proceedings of the British Machine Vision Conference (BMVC)},
  pages     = {84--88},
  year      = {2023}
}

@article{draelos2021machine,
  title={Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes},
  author={Draelos, Rachel Lea and Dov, David and Mazurowski, Maciej A and Lo, Joseph Y and Henao, Ricardo and Rubin, Geoffrey D and Carin, Lawrence},
  journal={Medical image analysis},
  volume={67},
  pages={101857},
  year={2021},
  publisher={Elsevier}
}

@misc{covid-ct,
  author    = {Ma, Jun and Ge, Cheng and Wang, Yixin and An, Xingle and Gao, Jiantao and Yu, Ziqi and Zhang, Minqing and Liu, Xin and Deng, Xueyuan and Cao, Shucheng and Wei, Hao and Mei, Sen and Yang, Xiaoyu and Nie, Ziwei and Li, Chen and Tian, Lu and Zhu, Yuntao and Zhu, Qiongjie and Dong, Guoqiang and He, Jian},
  title     = {COVID-19 CT Lung and Infection Segmentation Dataset (Version 1.0)},
  year      = {2020},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.3757476},
  note      = {[Data set]},
  url={https://doi.org/10.5281/zenodo.3757476}
}

@article{karimi2020deep,
  title={Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis},
  author={Karimi, Davood and Dou, Haoran and Warfield, Simon K and Gholipour, Ali},
  journal={Medical image analysis},
  volume={65},
  pages={101759},
  year={2020},
  publisher={Elsevier}
}

@article{Rolnick2017DeepLI,
  title={Deep Learning is Robust to Massive Label Noise},
  author={David Rolnick and Andreas Veit and Serge J. Belongie and Nir Shavit},
  journal={ArXiv},
  year={2017},
  volume={abs/1705.10694},
  url={https://api.semanticscholar.org/CorpusID:3511978}
}

@article{MUMOLI-COVID19,
  title={Lung Function and Symptoms in Post–COVID-19 Patients: A Single-Center Experience},
  author={Mumoli, Nicola and Bonaventura, Aldo and Colombo, Alessandra and Vecchié, Alessandra and Cei, Marco and Vitale, José and Pavan, Luca and Mazzone, Antonino and Dentali, Francesco},
  journal={Mayo Clinic Proceedings: Innovations, Quality \& Outcomes},
  volume={5},
  number={5},
  pages={907--915},
  year={2021},
  publisher={Elsevier}
}

@InProceedings{Xin_SegMamba_MICCAI2024,
        author = { Xing, Zhaohu and Ye, Tian and Yang, Yijun and Liu, Guang and Zhu, Lei},
        title = { { SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation } },
        booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
        year = {2024},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 15008},
        month = {October},
        page = {578 -- 588}
}

@article{RAD-ChestCT,
  title={Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes},
  author={Draelos, Rachel Lea and Dov, David and Mazurowski, Maciej A and Lo, Joseph Y and Henao, Ricardo and Rubin, Geoffrey D and Carin, Lawrence},
  journal={Medical image analysis},
  volume={67},
  pages={101857},
  year={2021},
  publisher={Elsevier}
}

@misc{sat,
      title={One Model to Rule them All: Towards Universal Segmentation for Medical Images with Text Prompts}, 
      author={Ziheng Zhao and Yao Zhang and Chaoyi Wu and Xiaoman Zhang and Ya Zhang and Yanfeng Wang and Weidi Xie},
      year={2024},
      eprint={2312.17183},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2312.17183}, 
}

@article{radbert,
  title={RadBERT: adapting transformer-based language models to radiology},
  author={Yan, An and McAuley, Julian and Lu, Xing and Du, Jiang and Chang, Eric Y and Gentili, Amilcare and Hsu, Chun-Nan},
  journal={Radiology: Artificial Intelligence},
  volume={4},
  number={4},
  pages={e210258},
  year={2022},
  publisher={Radiological Society of North America}
}

@article{report_method3,
  title={Automatic intracranial abnormality detection and localization in head CT scans by learning from free-text reports},
  author={Liu, Aohan and Guo, Yuchen and Lyu, Jinhao and Xie, Jing and Xu, Feng and Lou, Xin and Yong, Jun-hai and Dai, Qionghai},
  journal={Cell Reports Medicine},
  volume={4},
  number={9},
  year={2023},
  publisher={Elsevier}
}

@article{report_method2,  
  title = {Annotation-free multi-organ anomaly detection in abdominal CT using free-text radiology reports: a multi-centre retrospective study},  
  author = {Junya Sato and Kento Sugimoto and Yuki Suzuki and Tomohiro Wataya and Kosuke Kita and Daiki Nishigaki and Miyuki Tomiyama and Yu Hiraoka and Masatoshi Hori and Toshihiro Takeda and Shoji Kido and Noriyuki Tomiyama},  
  journal = {eBioMedicine},  
  volume = {110},  
  pages = {105463},  
  year = {2024},  
  issn = {2352-3964},  
  doi = {10.1016/j.ebiom.2024.105463},  
  url = {https://www.sciencedirect.com/science/article/pii/S2352396424004997},   
  keywords = {Artificial intelligence, Named-entity recognition, Radiology report, Anomaly detection, Computed tomography},  
  funding = {Japan Science and Technology Agency}  
}

@inproceedings{chen2024scunet++,
  title={SCUNet++: Swin-UNet and CNN Bottleneck Hybrid Architecture with Multi-Fusion Dense Skip Connection for Pulmonary Embolism CT Image Segmentation},
  author={Chen, Yifei and Zou, Binfeng and Guo, Zhaoxin and Huang, Yiyu and Huang, Yifan and Qin, Feiwei and Li, Qinhai and Wang, Changmiao},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={7759--7767},
  year={2024}
}

@misc{healthcareitnews2025,  
  author       = {Mike Miliard},  
  title        = {AI is transforming imaging, with FDA approvals continuing apace},  
  year         = {2024},  
  note         = {Accessed: 2025-03-07},  
  url          = {https://www.healthcareitnews.com/news/ai-transforming-imaging-fda-approvals-continuing-apace}  
}  

@article{Ronneberger2015UNetCN,
  title={U-Net: Convolutional Networks for Biomedical Image Segmentation},
  author={Olaf Ronneberger and Philipp Fischer and Thomas Brox},
  journal={ArXiv},
  year={2015},
  volume={abs/1505.04597},
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@article{fang2024gfe,
  title={GFE-Mamba: Mamba-based AD Multi-modal Progression Assessment via Generative Feature Extraction from MCI},
  author={Fang, Zhaojie and Zhu, Shenghao and Chen, Yifei and Zou, Binfeng and Jia, Fan and Qiu, Linwei and Liu, Chang and Huang, Yiyu and Feng, Xiang and Qin, Feiwei and others},
  journal={arXiv preprint arXiv:2407.15719},
  year={2024}
}
