% SECTION 2.1: VISION FOUNDATION MODELS
 @inproceedings{fm_clip,
  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{fm_dinov2,
  title={{DINO}v2: Learning robust visual features without supervision},
  author={Oquab, Maxime and Darcet, Timoth{\'e}e and Moutakanni, Th{\'e}o and Vo, Huy and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and others},
  journal={arXiv preprint arXiv:2304.07193},
  year={2023}
}

@article{fm_dinov3,
  title={{DINO}v3},
  author={Sim{\'e}oni, Oriane and Vo, Huy V and Seitzer, Maximilian and Baldassarre, Federico and Oquab, Maxime and Jose, Cijo and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun and Ramamonjisoa, Micha{\"e}l and others},
  journal={arXiv preprint arXiv:2508.10104},
  year={2025}
}

@inproceedings{fm_sam,
  title={Segment anything},
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C and Lo, Wan-Yen and others},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={4015--4026},
  year={2023}
}

% SECTION 2.1: KNOWLEDGE DISTILLATION

@article{kd_hinton,
  title={Distilling the Knowledge in a Neural Network},
  author={Hinton, Geoffrey and Vanhoucke, Vincent and Dean, Jeff},
  journal={arXiv preprint arXiv:1503.02531},
  year={2015}
}

@misc{kd_fitnets,
      title={FitNets: Hints for Thin Deep Nets}, 
      author={Adriana Romero and Nicolas Ballas and Samira Ebrahimi Kahou and Antoine Chassang and Carlo Gatta and Yoshua Bengio},
      year={2015},
      eprint={1412.6550},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1412.6550}, 
}

@article{kd_attention,
  title={Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer},
  author={Zagoruyko, Sergey and Komodakis, Nikos},
  journal={arXiv preprint arXiv:1612.03928},
  year={2016}
}


@article{kd_sam_distill,
  title={Faster segment anything: Towards lightweight sam for mobile applications},
  author={Zhang, Chaoning and Han, Dongshen and Qiao, Yu and Kim, Jung Uk and Bae, Sung-Ho and Lee, Seungkyu and Hong, Choong Seon},
  journal={arXiv preprint arXiv:2306.14289},
  year={2023}
}

@inproceedings{kd_dino_distill,
  title={Distilling self-supervised vision transformers for weakly-supervised few-shot classification \& segmentation},
  author={Kang, Dahyun and Koniusz, Piotr and Cho, Minsu and Murray, Naila},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={19627--19638},
  year={2023}
}

% SECTION 2.2: MEDICAL FOUNDATION MODELS

@article{medsam_paper,
  title={Segment anything in medical images},
  author={Ma, Jun and He, Yuting and Li, Feifei and Han, Lin and You, Chenyu and Wang, Bo},
  journal={Nature Communications},
  volume={15},
  number={1},
  pages={654},
  year={2024},
  publisher={Nature Publishing Group UK London}
}
@article{Sam2Rad,
  title={Sam2Rad: A segmentation model for medical images with learnable prompts},
  author={Wahd, Assefa Seyoum and Felfeliyan, Banafshe and Zhou, Yuyue and Ghosh, Shrimanti and McArthur, Adam and Zhang, Jiechen and Jaremko, Jacob L and Hareendranathan, Abhilash},
  journal={Computers in Biology and Medicine},
  volume={187},
  pages={109725},
  year={2025},
  publisher={Elsevier}
}

@inproceedings{med_fm_ct_mri,
  title={Towards foundation models learned from anatomy in medical imaging via self-supervision},
  author={Hosseinzadeh Taher, Mohammad Reza and Gotway, Michael B and Liang, Jianming},
  booktitle={MICCAI Workshop on Domain Adaptation and Representation Transfer},
  pages={94--104},
  year={2023},
  organization={Springer}
}
@article{fm_CT,
  title={Vision foundation models for computed tomography},
  author={Pai, Suraj and Hadzic, Ibrahim and Bontempi, Dennis and Bressem, Keno and Kann, Benjamin H and Fedorov, Andriy and Mak, Raymond H and Aerts, Hugo JWL},
  journal={arXiv preprint arXiv:2501.09001},
  year={2025}
}

@article{med_fm_hist,
  title={Prism: A multi-modal generative foundation model for slide-level histopathology},
  author={Shaikovski, George and Casson, Adam and Severson, Kristen and Zimmermann, Eric and Wang, Yi Kan and Kunz, Jeremy D and Retamero, Juan A and Oakley, Gerard and Klimstra, David and Kanan, Christopher and others},
  journal={arXiv preprint arXiv:2405.10254},
  year={2024}
}

% SECTION 2.2: ULTRASOUND FOUNDATION MODELS

@article{usfm1,
  title={USF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencoding},
  author={Megahed, Youssef and Ducharme, Robin and Walker, Mark and Hawken, Steven and Chan, Adrian DC},
  journal={arXiv preprint arXiv:2510.22990},
  year={2025}
}

@article{usfm2,
  title={Usfm: A universal ultrasound foundation model generalized to tasks and organs towards label efficient image analysis},
  author={Jiao, Jing and Zhou, Jin and Li, Xiaokang and Xia, Menghua and Huang, Yi and Huang, Lihong and Wang, Na and Zhang, Xiaofan and Zhou, Shichong and Wang, Yuanyuan and others},
  journal={Medical image analysis},
  volume={96},
  pages={103202},
  year={2024},
  publisher={Elsevier}
}

@article{usfm3,
  title={TinyUSFM: Towards Compact and Efficient Ultrasound Foundation Models},
  author={Ma, Chen and Jiao, Jing and Liang, Shuyu and Fu, Junhu and Wang, Qin and Li, Zeju and Wang, Yuanyuan and Guo, Yi},
  journal={arXiv preprint arXiv:2510.19239},
  year={2025}
}

% SECTION 2.3: ULTRASOUND DEEP LEARNING


@inproceedings{us_cnn1,
  title={Deep convolutional neural network for ultrasound image enhancement},
  author={Perdios, Dimitris and Vonlanthen, Manuel and Besson, Adrien and Martinez, Florian and Arditi, Marcel and Thiran, Jean-Philippe},
  booktitle={2018 IEEE International Ultrasonics Symposium (IUS)},
  pages={1--4},
  year={2018},
  organization={IEEE}
}

@article{us_cnn2,
  title={Segmentation of thyroid glands and nodules in ultrasound images using the improved U-Net architecture},
  author={Zheng, Tianlei and Qin, Hang and Cui, Yingying and Wang, Rong and Zhao, Weiguo and Zhang, Shijin and Geng, Shi and Zhao, Lei},
  journal={BMC Medical Imaging},
  volume={23},
  number={1},
  pages={56},
  year={2023},
  publisher={Springer}
}

@article{us_cnn3,
  title={Multi-class classification of thyroid nodules from automatic segmented ultrasound images: Hybrid ResNet based UNet convolutional neural network approach},
  author={Inan, Neslihan G{\"o}kmen and Kocada{\u{g}}l{\i}, Ozan and Y{\i}ld{\i}r{\i}m, D{\"u}zg{\"u}n and Me{\c{s}}e, {\.I}smail and Kovan, {\"O}zge},
  journal={Computer Methods and Programs in Biomedicine},
  volume={243},
  pages={107921},
  year={2024},
  publisher={Elsevier}
}

@article{us_unet_resnet1,
  title={{U-Net}: Convolutional Networks for Biomedical Image Segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  journal={International Conference on Medical Image Computing and Computer Assisted Intervention},
  pages={234--241},
  year={2015}
}

@article{us_unet_resnet2,
  title={{Encoder-Decoder} with Atrous Separable Convolution for Semantic Image Segmentation},
  author={Chen, Liang-Chieh and Zhu, Yukun and Papandreou, George and Schroff, Florian and Adam, Hartwig},
  journal={European Conference on Computer Vision},
  year={2018}
}

% SECTION 2.4: MEDICAL KNOWLEDGE DISTILLATION

@article{med_kd_seg,
  title={Efficient medical image segmentation based on knowledge distillation},
  author={Qin, Dian and Bu, Jia-Jun and Liu, Zhe and Shen, Xin and Zhou, Sheng and Gu, Jing-Jun and Wang, Zhi-Hua and Wu, Lei and Dai, Hui-Fen},
  journal={IEEE Transactions on Medical Imaging},
  volume={40},
  number={12},
  pages={3820--3831},
  year={2021},
  publisher={IEEE}
}

@article{med_kd_ssl1,
  title={A self-supervised guided knowledge distillation framework for unpaired low-dose CT image denoising},
  author={Wang, Jiping and Tang, Yufei and Wu, Zhongyi and Du, Qiang and Yao, Libing and Yang, Xiaodong and Li, Ming and Zheng, Jian},
  journal={Computerized medical imaging and graphics},
  volume={107},
  pages={102237},
  year={2023},
  publisher={Elsevier}
}

@article{med_kd_ssl2,
  title={Distill-soda: Distilling self-supervised vision transformer for source-free open-set domain adaptation in computational pathology},
  author={Vray, Guillaume and Tomar, Devavrat and Bozorgtabar, Behzad and Thiran, Jean-Philippe},
  journal={IEEE transactions on medical imaging},
  volume={43},
  number={5},
  pages={2021--2032},
  year={2024},
  publisher={IEEE}
}

@article{us_kd_task_specific,
  title={Knowledge distillation for efficient standard scanplane detection of fetal ultrasound},
  author={Dapueto, Jacopo and Zini, Luca and Odone, Francesca},
  journal={Medical \& Biological Engineering \& Computing},
  volume={62},
  number={1},
  pages={73--82},
  year={2024},
  publisher={Springer}
}

% ADDITIONAL: DDTI DATASET CITATION (from your paper)

@inproceedings{pedraza2015ddti,
  title={An open access thyroid ultrasound image database},
  author={Pedraza, Lina and Vargas, Carlos and Narv{\'a}ez, Fabi{\'a}n and Dur{\'a}n, Oscar and Mu{\~n}oz, Emma and Romero, Eduardo},
  booktitle={10th International symposium on medical information processing and analysis},
  volume={9287},
  pages={188--193},
  year={2015},
  organization={SPIE}
}
@article{BUSI,
  title={Dataset of breast ultrasound images},
  author={Al-Dhabyani, Walid and Gomaa, Mohammed and Khaled, Hussien and Fahmy, Aly},
  journal={Data in brief},
  volume={28},
  pages={104863},
  year={2020},
  publisher={Elsevier}
}

@article{pocus,
  title={Artificial intelligence (AI) applications for point of care ultrasound (POCUS) in low-resource settings: a scoping review},
  author={Kim, Seungjun and Fischetti, Chanel and Guy, Megan and Hsu, Edmund and Fox, John and Young, Sean D},
  journal={Diagnostics},
  volume={14},
  number={15},
  pages={1669},
  year={2024},
  publisher={MDPI}
}
@article{ViT_1,
  title={Vision transformers on the edge: A comprehensive survey of model compression and acceleration strategies},
  author={Saha, Shaibal and Xu, Lanyu},
  journal={Neurocomputing},
  pages={130417},
  year={2025},
  publisher={Elsevier}
}

@article{CNN_1,
  title={Improving the generalizability and performance of an ultrasound deep learning model using limited multicenter data for lung sliding artifact identification},
  author={Wu, Derek and Smith, Delaney and VanBerlo, Blake and Roshankar, Amir and Lee, Hoseok and Li, Brian and Ali, Faraz and Rahman, Marwan and Basmaji, John and Tschirhart, Jared and others},
  journal={Diagnostics},
  volume={14},
  number={11},
  pages={1081},
  year={2024},
  publisher={MDPI}
}

"@article{hann2017algorithm,
  title={Algorithm guided outlining of 105 pancreatic cancer liver metastases in Ultrasound},
  author={Hann, Alexander and Bettac, Lucas and Haenle, Mark M and Graeter, Tilmann and Berger, Andreas W and Dreyhaupt, Jens and Schmalstieg, Dieter and Zoller, Wolfram G and Egger, Jan},
  journal={Scientific Reports},
  volume={7},
  number={1},
  pages={12779},
  year={2017},
  publisher={Nature Publishing Group UK London}
}"
"@article{vitale2020improving,
  title={Improving realism in patient-specific abdominal ultrasound simulation using CycleGANs},
  author={Vitale, Santiago and Orlando, Jos{\'e} Ignacio and Iarussi, Emmanuel and Larrabide, Ignacio},
  journal={International journal of computer assisted radiology and surgery},
  volume={15},
  number={2},
  pages={183--192},
  year={2020},
  publisher={Springer}
}"
"@misc{yiming2022annotated,
  title={Annotated ultrasound liver images},
  author={Yiming, Xu and Bowen, Z and Xiaohong, L and Tao, W and Jinxiu, J and Shijie, W and Yufan, L and Hongjun, Z and Tong, L and Ye, S and others},
  year={2022},
  publisher={Zenodo}
}"
"@inproceedings{tyagi2024nerve,
  title={Nerve block target localization and needle guidance for autonomous robotic ultrasound guided regional anesthesia},
  author={Tyagi, Abhishek and Tyagi, Abhay and Kaur, Manpreet and Aggarwal, Richa and Soni, Kapil D and Sivaswamy, Jayanthi and Trikha, Anjan},
  booktitle={2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={5867--5872},
  year={2024},
  organization={IEEE}
}"
"@article{pawlowska2024curated,
  title={Curated benchmark dataset for ultrasound based breast lesion analysis},
  author={Paw{\l}owska, Anna and {\'C}wierz-Pie{\'n}kowska, Anna and Domalik, Agnieszka and Jagu{\'s}, Dominika and Kasprzak, Piotr and Matkowski, Rafa{\l} and Fura, {\L}ukasz and Nowicki, Andrzej and {\.Z}o{\l}ek, Norbert},
  journal={Scientific Data},
  volume={11},
  number={1},
  pages={148},
  year={2024},
  publisher={Nature Publishing Group UK London}
}"
"@article{vallez2025bus,
  title={BUS-UCLM: Breast ultrasound lesion segmentation dataset},
  author={Vallez, Noelia and Bueno, Gloria and Deniz, Oscar and Rienda, Miguel Angel and Pastor, Carlos},
  journal={Scientific Data},
  volume={12},
  number={1},
  pages={242},
  year={2025},
  publisher={Nature Publishing Group UK London}
}"
"@article{gomez2024bus,
  title={BUS-BRA: a breast ultrasound dataset for assessing computer-aided diagnosis systems},
  author={G{\'o}mez-Flores, Wilfrido and Gregorio-Calas, Maria Julia and Coelho de Albuquerque Pereira, Wagner},
  journal={Medical Physics},
  volume={51},
  number={4},
  pages={3110--3123},
  year={2024},
  publisher={Wiley Online Library}
}"
"@article{iqbal2024memory,
  title={Memory-efficient transformer network with feature fusion for breast tumor segmentation and classification task},
  author={Iqbal, Ahmed and Sharif, Muhammad},
  journal={Engineering Applications of Artificial Intelligence},
  volume={127},
  pages={107292},
  year={2024},
  publisher={Elsevier}
}"
"@article{elmekki2025cactus,
  title={CACTUS: An open dataset and framework for automated Cardiac Assessment and Classification of Ultrasound images using deep transfer learning},
  author={Elmekki, Hanae and Alagha, Ahmed and Sami, Hani and Spilkin, Amanda and Zanuttini, Antonela Mariel and Zakeri, Ehsan and Bentahar, Jamal and Kadem, Lyes and Xie, Wen-Fang and Pibarot, Philippe and others},
  journal={Computers in Biology and Medicine},
  volume={190},
  pages={110003},
  year={2025},
  publisher={Elsevier}
}"
"@article{leclerc2019deep,
  title={Deep learning for segmentation using an open large-scale dataset in 2D echocardiography},
  author={Leclerc, Sarah and Smistad, Erik and Pedrosa, Joao and {\O}stvik, Andreas and Cervenansky, Frederic and Espinosa, Florian and Espeland, Torvald and Berg, Erik Andreas Rye and Jodoin, Pierre-Marc and Grenier, Thomas and others},
  journal={IEEE transactions on medical imaging},
  volume={38},
  number={9},
  pages={2198--2210},
  year={2019},
  publisher={IEEE}
}"
"@article{momot2022common,
  title={Common carotid artery ultrasound images},
  author={Momot, Agata},
  journal={Mendeley Data},
  year={2022}
}"
"@article{wiedemann2025covid,
  author={Wiedemann, Nina and Boer, Dianne de Korte-de and Richter, Matthias and van de Weijer, Sjors and Buhre, Charlotte and Eggert, Franz A. M. and Aarnoudse, Sophie and Grevendonk, Lotte and Röber, Steffen and Remie, Carlijn M.E. and Buhre, Wolfgang and Henry, Ronald and Born, Jannis},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={COVID-BLUeS - A Prospective Study on the Value of AI in Lung Ultrasound Analysis}, 
  year={2025},
  volume={29},
  number={9},
  pages={6301-6310},
  keywords={COVID-19;Artificial intelligence;Videos;Lungs;Ultrasonic imaging;Analytical models;Pathology;Medical diagnostic imaging;Data models;Training;Lung ultrasound;computer vision;COVID-19},
  doi={10.1109/JBHI.2025.3543686}
}"
"@inproceedings{xu2020imagechd,
  title={Imagechd: A 3d computed tomography image dataset for classification of congenital heart disease},
  author={Xu, Xiaowei and Wang, Tianchen and Zhuang, Jian and Yuan, Haiyun and Huang, Meiping and Cen, Jianzheng and Jia, Qianjun and Dong, Yuhao and Shi, Yiyu},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={77--87},
  year={2020},
  organization={Springer}
}"
"@article{ouyang2020video,
  title={Video-based AI for beat-to-beat assessment of cardiac function},
  author={Ouyang, David and He, Bryan and Ghorbani, Amirata and Yuan, Neal and Ebinger, Joseph and Langlotz, Curtis P and Heidenreich, Paul A and Harrington, Robert A and Liang, David H and Ashley, Euan A and others},
  journal={Nature},
  volume={580},
  number={7802},
  pages={252--256},
  year={2020},
  publisher={Nature Publishing Group}
}"
"@article{ashkani2022fast,
  title={Fast and accurate U-net model for fetal ultrasound image segmentation},
  author={Ashkani Chenarlogh, Vahid and Ghelich Oghli, Mostafa and Shabanzadeh, Ali and Sirjani, Nasim and Akhavan, Ardavan and Shiri, Isaac and Arabi, Hossein and Sanei Taheri, Morteza and Tarzamni, Mohammad Kazem},
  journal={Ultrasonic imaging},
  volume={44},
  number={1},
  pages={25--38},
  year={2022},
  publisher={SAGE Publications Sage CA: Los Angeles, CA}
}"
"@article{da2023fetal,
  title={Fetal abdominal structures segmentation dataset using ultrasonic images},
  author={Da, C and others},
  journal={Mendeley Data},
  year={2023}
}"
"@inproceedings{stoean2021assessment,
  title={An assessment of the usefulness of image pre-processing for the classification of first trimester fetal heart ultrasound using convolutional neural networks},
  author={Stoean, Catalin and Stoean, Ruxandra and Hotoleanu, Mircea and Iliescu, Dominic and Patru, Ciprian and Nagy, Rodica},
  booktitle={2021 25th International Conference on System Theory, Control and Computing (ICSTCC)},
  pages={242--248},
  year={2021},
  organization={IEEE}
}"
"@article{burgos2020evaluation,
  title={Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes},
  author={Burgos-Artizzu, Xavier P and Coronado-Guti{\'e}rrez, David and Valenzuela-Alcaraz, Brenda and Bonet-Carne, Elisenda and Eixarch, Elisenda and Crispi, Fatima and Gratac{\'o}s, Eduard},
  journal={Scientific Reports},
  volume={10},
  number={1},
  pages={10200},
  year={2020},
  publisher={Nature Publishing Group UK London}
}"
"@article{he2022query2,
  title={Query2: Query over queries for improving gastrointestinal stromal tumour detection in an endoscopic ultrasound},
  author={He, Qi and Bano, Sophia and Liu, Jing and Liu, Wentian and Stoyanov, Danail and Zuo, Siyang},
  journal={Computers in Biology and Medicine},
  pages={106424},
  year={2022},
  publisher={Elsevier}
}"
"@article{van2018automated,
  title={Automated measurement of fetal head circumference using 2D ultrasound images},
  author={van den Heuvel, Thomas LA and de Bruijn, Dagmar and de Korte, Chris L and Ginneken, Bram van},
  journal={PloS one},
  volume={13},
  number={8},
  pages={e0200412},
  year={2018},
  publisher={Public Library of Science San Francisco, CA USA}
}"
"@article{kumar2025novel,
  title={A novel open-source ultrasound dataset with deep learning benchmarks for spinal cord injury localization and anatomical segmentation},
  author={Kumar, Avisha and Kotkar, Kunal and Jiang, Kelly and Bhimreddy, Meghana and Davidar, Daniel and Weber-Levine, Carly and Krishnan, Siddharth and Kerensky, Max J and Liang, Ruixing and Leadingham, Kelley K and others},
  journal={Scientific reports},
  volume={15},
  number={1},
  pages={33192},
  year={2025},
  publisher={Nature Publishing Group UK London}
}"
"@article{lu2022jnu,
  title={The JNU-IFM dataset for segmenting pubic symphysis-fetal head},
  author={Lu, Yaosheng and Zhou, Mengqiang and Zhi, Dengjiang and Zhou, Minghong and Jiang, Xiaosong and Qiu, Ruiyu and Ou, Zhanhong and Wang, Huijin and Qiu, Di and Zhong, Mei and others},
  journal={Data in brief},
  volume={41},
  pages={107904},
  year={2022},
  publisher={Elsevier}
}"
"@inproceedings{singla2023open,
  title={The open kidney ultrasound data set},
  author={Singla, Rohit and Ringstrom, Cailin and Hu, Grace and Lessoway, Victoria and Reid, Janice and Nguan, Christopher and Rohling, Robert},
  booktitle={International Workshop on Advances in Simplifying Medical Ultrasound},
  pages={155--164},
  year={2023},
  organization={Springer}
}"
"@article{mclaughlan2024lung,
  title={Lung ultrasound covid phantom dataset used for training machine learning model},
  author={McLaughlan, James R and Howell, Lewis and Ingram, Nicola},
  year={2024},
  publisher={University of Leeds}
}"
"@article{shao2024micro,
  title={Micro-ultrasound prostate segmentation dataset},
  author={Shao, W and Brisbane, W},
  journal={URL: https://doi. org/10.5281/zenodo},
  volume={10475293},
  year={2024}
}"
"@article{DBLP:journals/corr/abs-2207-06799,
  author    = {Qi Zhao and
               Shuchang Lyu and
               Wenpei Bai and
               Linghan Cai and
               Binghao Liu and
               Meijing Wu and
               Xiubo Sang and
               Min Yang and
               Lijiang Chen},
  title     = {A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised
               Cross-Domain Semantic Segmentation},
  journal   = {CoRR},
  volume    = {abs/2207.06799},
  year      = {2022},
}"

"@misc{jieyun2024pubic,
  title={Pubic symphysis-fetal head segmentation and angle of progression},
  author={Jieyun, Bai and ZhanHong, Ou},
  year={2024}
}"
"@inproceedings{baum2023mr,
  title={MR to ultrasound registration for prostate challenge-dataset},
  author={Baum, Zachary and Saeed, Shaheer and Min, Zhe and Hu, Yipeng and Barratt, Dean},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI},
  volume={2023},
  year={2023}
}"
"@article{guo2021segmentation,
  title={Segmentation and recognition of breast ultrasound images based on an expanded U-Net},
  author={Guo, Yanjun and Duan, Xingguang and Wang, Chengyi and Guo, Huiqin},
  journal={Plos one},
  volume={16},
  number={6},
  pages={e0253202},
  year={2021},
  publisher={Public Library of Science San Francisco, CA USA}
}"
"@article{kronke2022tracked,
  title={Tracked 3D ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry},
  author={Kr{\""o}nke, Markus and Eilers, Christine and Dimova, Desislava and K{\""o}hler, Melanie and Buschner, Gabriel and Schweiger, Lilit and Konstantinidou, Lemonia and Makowski, Marcus and Nagarajah, James and Navab, Nassir and others},
  journal={Plos one},
  volume={17},
  number={7},
  pages={e0268550},
  year={2022},
  publisher={Public Library of Science San Francisco, CA USA}
}"
"@article{marzola2021deep,
  title={Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment},
  author={Marzola, Francesco and Van Alfen, Nens and Doorduin, Jonne and Meiburger, Kristen M},
  journal={Computers in Biology and Medicine},
  volume={135},
  pages={104623},
  year={2021},
  publisher={Elsevier}
}"
"@misc{xbhlk_STU-Hospital_2026,
  author       = {{xbhlk}},
  title        = {{STU-Hospital}},
  year         = {2026},
  howpublished = {\url{https://github.com/xbhlk/STU-Hospital}},
  note         = {GitHub repository, accessed 23 Jan 2026},
}
"
"@article{anitha2024ultrasound,
  title={Ultrasound fetus dataset},
  author={Anitha, A},
  journal={Mendeley Data},
  volume={1},
  year={2024}
}"
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