

@article{r1,
  title={Deep Learning--Based Fluence Map Prediction for Pancreas Stereotactic Body Radiation Therapy With Simultaneous Integrated Boost},
  author={Wang, Wentao and Sheng, Yang and Palta, Manisha and Czito, Brian and Willett, Christopher and Hito, Martin and Yin, Fang-Fang and Wu, Qiuwen and Ge, Yaorong and Wu, Q Jackie},
  journal={Advances in radiation oncology},
  volume={6},
  number={4},
  pages={100672},
  year={2021},
  publisher={Elsevier}
}

@article{r2,
  title={Accelerate treatment planning process using deep learning generated fluence maps for cervical cancer radiation therapy},
  author={Yuan, Zengtai and Wang, Yuxiang and Hu, Panpan and Zhang, Duoer and Yan, Bing and Lu, Hsiao-Ming and Zhang, Hongyan and Yang, Yidong},
  journal={Medical Physics},
  volume={49},
  number={4},
  pages={2631--2641},
  year={2022},
  publisher={Wiley Online Library}
}

@article{r3,
  title={Fluence map prediction using deep learning models--direct plan generation for pancreas stereotactic body radiation therapy},
  author={Wang, Wentao and Sheng, Yang and Wang, Chunhao and Zhang, Jiahan and Li, Xinyi and Palta, Manisha and Czito, Brian and Willett, Christopher G and Wu, Qiuwen and Ge, Yaorong and others},
  journal={Frontiers in artificial intelligence},
  volume={3},
  pages={68},
  year={2020},
  publisher={Frontiers Media SA}
}


@article{r4,
  title={Treatment plan prediction for lung IMRT using deep learning based fluence map generation},
  author={Vandewinckele, Liesbeth and Willems, Siri and Lambrecht, Maarten and Berkovic, Patrick and Maes, Frederik and Crijns, Wouter},
  journal={Physica Medica},
  volume={99},
  pages={44--54},
  year={2022},
  publisher={Elsevier}
}

@article{r5,
  title={Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning algorithm for real-time prostate treatment planning},
  author={Li, Xinyi and Zhang, Jiahan and Sheng, Yang and Chang, Yushi and Yin, Fang-Fang and Ge, Yaorong and Wu, Q Jackie and Wang, Chunhao},
  journal={Physics in Medicine \& Biology},
  volume={65},
  number={17},
  pages={175014},
  year={2020},
  publisher={IOP Publishing}
}



@article{r6,
  title={Generalizability of deep learning based fluence map prediction as an inverse planning approach},
  author={Ma, Lin and Chen, Mingli and Gu, Xuejun and Lu, Weiguo},
  journal={arXiv preprint arXiv:2104.15032},
  year={2021}
}

@article{r7,
  title={Deep learning-based inverse mapping for fluence map prediction},
  author={Ma, Lin and Chen, Mingli and Gu, Xuejun and Lu, Weiguo},
  journal={Physics in Medicine \& Biology},
  volume={65},
  number={23},
  pages={235035},
  year={2020},
  publisher={IOP Publishing}
}

@inproceedings{r8,
  title={Unetr: Transformers for 3d medical image segmentation},
  author={Hatamizadeh, Ali and Tang, Yucheng and Nath, Vishwesh and Yang, Dong and Myronenko, Andriy and Landman, Bennett and Roth, Holger R and Xu, Daguang},
  booktitle={Proceedings of the IEEE/CVF winter conference on applications of computer vision},
  pages={574--584},
  year={2022}
}

@inproceedings{r9,
  title={Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images},
  author={Hatamizadeh, Ali and Nath, Vishwesh and Tang, Yucheng and Yang, Dong and Roth, Holger R and Xu, Daguang},
  booktitle={International MICCAI brainlesion workshop},
  pages={272--284},
  year={2021},
  organization={Springer}
}

@article{r10,
  title={A cascade transformer-based model for 3D dose distribution prediction in head and neck cancer radiotherapy},
  author={Gheshlaghi, Tara and Nabavi, Shahabedin and Shirzadikia, Samireh and Moghaddam, Mohsen Ebrahimi and Rostampour, Nima},
  journal={Physics in Medicine \& Biology},
  volume={69},
  number={4},
  pages={045010},
  year={2024},
  publisher={IOP Publishing}
}

@article{r11,
  title={TrDosePred: A deep learning dose prediction algorithm based on transformers for head and neck cancer radiotherapy},
  author={Hu, Chenchen and Wang, Haiyun and Zhang, Wenyi and Xie, Yaoqin and Jiao, Ling and Cui, Songye},
  journal={Journal of Applied Clinical Medical Physics},
  volume={24},
  number={7},
  pages={e13942},
  year={2023},
  publisher={Wiley Online Library}
}

@inproceedings{r12,
  title={Latent Spaces Enable Transformer-Based Dose Prediction in Complex Radiotherapy Plans},
  author={Wang, Edward and Au, Ryan and Lang, Pencilla and Mattonen, Sarah A},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={361--371},
  year={2024},
  organization={Springer}
}

@article{r13,
  title={A transformer-embedded multi-task model for dose distribution prediction},
  author={Wen, Lu and Xiao, Jianghong and Tan, Shuai and Wu, Xi and Zhou, Jiliu and Peng, Xingchen and Wang, Yan},
  journal={International Journal of Neural Systems},
  volume={33},
  number={08},
  pages={2350043},
  year={2023},
  publisher={World Scientific}
}

@article{r14,
  title={Ultra-fast, one-click radiotherapy treatment planning outside a treatment planning system},
  author={Heilemann, Gerd and Zimmermann, Lukas and Nyholm, Tufve and Simk{\'o}, Attila and Widder, Joachim and Goldner, Gregor and Georg, Dietmar and Kuess, Peter},
  journal={Physics and Imaging in Radiation Oncology},
  volume={33},
  pages={100724},
  year={2025},
  publisher={Elsevier}
}
@article{r15,
  title={nnformer: Interleaved transformer for volumetric segmentation},
  author={Zhou, Hong-Yu and Guo, Jiansen and Zhang, Yinghao and Yu, Lequan and Wang, Liansheng and Yu, Yizhou},
  journal={arXiv preprint arXiv:2109.03201},
  year={2021}
}

@inproceedings{r16,
  title={Med-former: A transformer based architecture for medical image classification},
  author={Chowdary, G Jignesh and Yin, Zhaozheng},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={448--457},
  year={2024},
  organization={Springer}
}

@ARTICLE{r17,
  author={Zhao, Hang and Gallo, Orazio and Frosio, Iuri and Kautz, Jan},
  journal={IEEE Transactions on Computational Imaging}, 
  title={Loss Functions for Image Restoration With Neural Networks}, 
  year={2017},
  volume={3},
  number={1},
  pages={47-57},
  keywords={Neural networks;Measurement;Image quality;Image restoration;Image processing;image restoration;neural networks;loss functions},
  doi={10.1109/TCI.2016.2644865}}

@article{r18,
  title={A comprehensive survey of loss functions and metrics in deep learning},
  author={Terven, Juan and Cordova-Esparza, Diana-Margarita and Romero-Gonz{\'a}lez, Julio-Alejandro and Ram{\'\i}rez-Pedraza, Alfonso and Ch{\'a}vez-Urbiola, EA},
  journal={Artificial Intelligence Review},
  volume={58},
  number={7},
  pages={195},
  year={2025},
  publisher={Springer}
}

@ARTICLE{r19,
  author={Ferdousi, Tanvir and Cohnstaedt, Lee W. and Scoglio, Caterina M.},
  journal={IEEE Access}, 
  title={A Windowed Correlation-Based Feature Selection Method to Improve Time Series Prediction of Dengue Fever Cases}, 
  year={2021},
  volume={9},
  number={},
  pages={141210-141222},
  keywords={Viruses (medical);Temperature distribution;Land surface temperature;Correlation;Ocean temperature;Time series analysis;Data models;Dengue;feature selection;machine learning;recurrent neural networks;time series},
  doi={10.1109/ACCESS.2021.3120309}}

@ARTICLE{r20,
  author={Zhou Wang and Bovik, A.C. and Sheikh, H.R. and Simoncelli, E.P.},
  journal={IEEE Transactions on Image Processing}, 
  title={Image quality assessment: from error visibility to structural similarity}, 
  year={2004},
  volume={13},
  number={4},
  pages={600-612},
  keywords={Image quality;Humans;Transform coding;Visual system;Visual perception;Data mining;Layout;Quality assessment;Degradation;Indexes},
  doi={10.1109/TIP.2003.819861}}

@inproceedings{r21,
  title={Photo-realistic single image super-resolution using a generative adversarial network},
  author={Ledig, Christian and Theis, Lucas and Husz{\'a}r, Ferenc and Caballero, Jose and Cunningham, Andrew and Acosta, Alejandro and Aitken, Andrew and Tejani, Alykhan and Totz, Johannes and Wang, Zehan and others},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4681--4690},
  year={2017}
}

@inproceedings{r22,
  title={Residual dense network for image super-resolution},
  author={Zhang, Yulun and Tian, Yapeng and Kong, Yu and Zhong, Bineng and Fu, Yun},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2472--2481},
  year={2018}
}

@article{r23,
  title={IMRT: a review and preview},
  author={Bortfeld, Thomas},
  journal={Physics in Medicine \& Biology},
  volume={51},
  number={13},
  pages={R363},
  year={2006},
  publisher={IOP Publishing}
}

@incollection{r24,
  title={Fluence map optimization in IMRT cancer treatment planning and a geometric approach},
  author={Zhang, Yin and Merritt, Michael},
  booktitle={Multiscale Optimization Methods and Applications},
  pages={205--227},
  year={2006},
  publisher={Springer}
}

@article{r25,
  title={Commissioning and quality assurance for MLC-based IMRT},
  author={Saw, Cheng B and Ayyangar, Komanduri M and Zhen, Weining and Thompson, Robert B and Enke, Charles A},
  journal={Medical Dosimetry},
  volume={26},
  number={2},
  pages={125--133},
  year={2001},
  publisher={Elsevier}
}

@inproceedings{r26,
  title={Focalunetr: A focal transformer for boundary-aware prostate segmentation using ct images},
  author={Li, Chengyin and Qiang, Yao and Sultan, Rafi Ibn and Bagher-Ebadian, Hassan and Khanduri, Prashant and Chetty, Indrin J and Zhu, Dongxiao},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={592--602},
  year={2023},
  organization={Springer}
}

@inproceedings{r27,
  title={Mulmodseg: Enhancing unpaired multi-modal medical image segmentation with modality-conditioned text embedding and alternating training},
  author={Li, Chengyin and Zhu, Hui and Sultan, Rafi Ibn and Ebadian, Hassan Bagher and Khanduri, Prashant and Indrin, Chetty and Thind, Kundan and Zhu, Dongxiao},
  booktitle={2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  pages={3581--3591},
  year={2025},
  organization={IEEE}
}

@inproceedings{r28,
  title={Autoprosam: Automated prompting sam for 3d multi-organ segmentation},
  author={Li, Chengyin and Sultan, Rafi Ibn and Khanduri, Prashant and Qiang, Yao and Indrin, Chetty and Zhu, Dongxiao},
  booktitle={2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  pages={3570--3580},
  year={2025},
  organization={IEEE}
}

@InProceedings{r29,
    author    = {Huang, Zhe and Jiang, Ruijie and Aeron, Shuchin and Hughes, Michael C.},
    title     = {Systematic Comparison of Semi-supervised and Self-supervised Learning for Medical Image Classification},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {22282-22293}
}

@misc{r30,
      title={MedMamba: Vision Mamba for Medical Image Classification}, 
      author={Yubiao Yue and Zhenzhang Li},
      year={2024},
      eprint={2403.03849},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2403.03849}, 
}

@article{r31,
  title={Evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer},
  author={Bakx, Nienke and van der Sangen, Maurice and Theuws, Jacqueline and Bluemink, Johanna and Hurkmans, Coen},
  journal={Physics and Imaging in Radiation Oncology},
  volume={28},
  pages={100496},
  year={2023},
  publisher={Elsevier}
}

@article{r32,
  title={Evaluation of deep learning-based automated radiotherapy planning for early-stage lung cancer using SBRT-VMAT: A comparison with manual planning},
  author={Nemoto, Hikaru and Saito, Masahide and Kadoya, Noriyuki and Komiyama, Takafumi and Tozuka, Ryota and Onishi, Hiroshi and Jingu, Keiichi},
  journal={Journal of Applied Clinical Medical Physics},
  volume={26},
  number={10},
  pages={e70291},
  year={2025},
  publisher={Wiley Online Library}
}

@misc{r33,
      title={A Beam's Eye View to Fluence Maps 3D Network for Ultra Fast VMAT Radiotherapy Planning}, 
      author={Simon Arberet and Florin C. Ghesu and Riqiang Gao and Martin Kraus and Jonathan Sackett and Esa Kuusela and Ali Kamen},
      year={2025},
      eprint={2502.03360},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2502.03360}, 
}

@article{r34,
  title={Automatic IMRT treatment planning through fluence prediction and plan fine-tuning for nasopharyngeal carcinoma},
  author={Cai, Wenwen and Ding, Shouliang and Li, Huali and Zhou, Xuanru and Dou, Wen and Zhou, Linghong and Song, Ting and Li, Yongbao},
  journal={Radiation Oncology},
  volume={19},
  number={1},
  pages={39},
  year={2024},
  publisher={Springer}
}

@article{r35,
  title={Voxel-based automatic multi-criteria optimization for intensity modulated radiation therapy},
  author={Mai, Yanhua and Kong, Fantu and Yang, Yiwei and Zhou, Linghong and Li, Yongbao and Song, Ting},
  journal={Radiation Oncology},
  volume={13},
  number={1},
  pages={241},
  year={2018},
  publisher={Springer}
}

@article{r36,
  title={Monai: An open-source framework for deep learning in healthcare},
  author={Cardoso, M Jorge and Li, Wenqi and Brown, Richard and Ma, Nic and Kerfoot, Eric and Wang, Yiheng and Murrey, Benjamin and Myronenko, Andriy and Zhao, Can and Yang, Dong and others},
  journal={arXiv preprint arXiv:2211.02701},
  year={2022}
}

@article{r37,
  title={Fluence Map Prediction with Deep Learning: A Transformer-based Approach},
  author={Mgboh, Ujunwa and Sultan, Rafi and Zhu, Dongxiao and Kim, Joshua},
  journal={arXiv preprint arXiv:2511.08645},
  year={2025}
}

@article{r38,
  title={A data-scalable transformer for medical image segmentation: architecture, model efficiency, and benchmark},
  author={Gao, Yunhe and Zhou, Mu and Liu, Di and Yan, Zhennan and Zhang, Shaoting and Metaxas, Dimitris N},
  journal={arXiv preprint arXiv:2203.00131},
  year={2022}
}

@inproceedings{r39,
  title={Focalunetr: A focal transformer for boundary-aware prostate segmentation using ct images},
  author={Li, Chengyin and Qiang, Yao and Sultan, Rafi Ibn and Bagher-Ebadian, Hassan and Khanduri, Prashant and Chetty, Indrin J and Zhu, Dongxiao},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={592--602},
  year={2023},
  organization={Springer}
}

@article{r40,
  title={Vispi: Automatic visual perception and interpretation of chest x-rays},
  author={Li, Xin and Cao, Rui and Zhu, Dongxiao},
  journal={arXiv preprint arXiv:1906.05190},
  year={2019}
}