% 1. Introduction
@article{harvey2010osteoporosis,
  title={Osteoporosis: impact on health and economics},
  author={Harvey, Nicholas and Dennison, Elaine and Cooper, Cyrus},
  journal={Nature Reviews Rheumatology},
  volume={6},
  number={2},
  pages={99--105},
  year={2010},
  publisher={Nature Publishing Group UK London}
}

@article{carberry2013unreported,
  title={Unreported vertebral body compression fractures at abdominal multidetector CT},
  author={Carberry, George A and Pooler, B Dustin and Binkley, Neil and Lauder, Travis B and Bruce, Richard J and Pickhardt, Perry J},
  journal={Radiology},
  volume={268},
  number={1},
  pages={120--126},
  year={2013},
  publisher={Radiological Society of North America, Inc.}
}

@inproceedings{husseini2020grading,
  title={Grading loss: a fracture grade-based metric loss for vertebral fracture detection},
  author={Husseini, Malek and Sekuboyina, Anjany and Loeffler, Maximilian and Navarro, Fernando and Menze, Bjoern H and Kirschke, Jan S},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2020: 23rd International Conference, Lima, Peru, October 4--8, 2020, Proceedings, Part VI 23},
  pages={733--742},
  year={2020},
  organization={Springer}
}

@inproceedings{engstler2022interpretable,
  title={Interpretable vertebral fracture diagnosis},
  author={Engstler, Paul and Keicher, Matthias and Schinz, David and Mach, Kristina and Gersing, Alexandra S and Foreman, Sarah C and Goller, Sophia S and Weissinger, Juergen and Rischewski, Jon and Dietrich, Anna-Sophia and others},
  booktitle={International Workshop on Interpretability of Machine Intelligence in Medical Image Computing},
  pages={71--81},
  year={2022},
  organization={Springer}
}

@article{keicher2023semantic,
  title={Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading},
  author={Keicher, Matthias and Atad, Matan and Schinz, David and Gersing, Alexandra S and Foreman, Sarah C and Goller, Sophia S and Weissinger, Juergen and Rischewski, Jon and Dietrich, Anna-Sophia and Wiestler, Benedikt and others},
  journal={arXiv preprint arXiv:2303.12031},
  year={2023}
}

@article{shin2016deep,
  title={Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning},
  author={Shin, Hoo-Chang and Roth, Holger R and Gao, Mingchen and Lu, Le and Xu, Ziyue and Nogues, Isabella and Yao, Jianhua and Mollura, Daniel and Summers, Ronald M},
  journal={IEEE transactions on medical imaging},
  volume={35},
  number={5},
  pages={1285--1298},
  year={2016},
  publisher={IEEE}
}



% 2. Related Work
% Vertebral Fracture Detection
@inproceedings{bar2017compression,
  title={Compression fractures detection on CT},
  author={Bar, Amir and Wolf, Lior and Amitai, Orna Bergman and Toledano, Eyal and Elnekave, Eldad},
  booktitle={Medical imaging 2017: computer-aided diagnosis},
  volume={10134},
  pages={1036--1043},
  year={2017},
  organization={SPIE}
}

@article{tomita2018deep,
  title={Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans},
  author={Tomita, Naofumi and Cheung, Yvonne Y and Hassanpour, Saeed},
  journal={Computers in biology and medicine},
  volume={98},
  pages={8--15},
  year={2018},
  publisher={Elsevier}
}

@inproceedings{nicolaes2020detection,
  title={Detection of vertebral fractures in CT using 3D convolutional neural networks},
  author={Nicolaes, Joeri and Raeymaeckers, Steven and Robben, David and Wilms, Guido and Vandermeulen, Dirk and Libanati, Cesar and Debois, Marc},
  booktitle={Computational Methods and Clinical Applications for Spine Imaging: 6th International Workshop and Challenge, CSI 2019, Shenzhen, China, October 17, 2019, Proceedings 6},
  pages={3--14},
  year={2020},
  organization={Springer}
}

@inproceedings{chettrit20203d,
  title={3D convolutional sequence to sequence model for vertebral compression fractures identification in CT},
  author={Chettrit, David and Meir, Tomer and Lebel, Hila and Orlovsky, Mila and Gordon, Ronen and Akselrod-Ballin, Ayelet and Bar, Amir},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2020: 23rd International Conference, Lima, Peru, October 4--8, 2020, Proceedings, Part VI 23},
  pages={743--752},
  year={2020},
  organization={Springer}
}

@article{chlkad2023deep,
  title={Deep Learning and Cloud-Based Computation for Cervical Spine Fracture Detection System},
  author={Ch{\l}ad, Pawe{\l} and Ogiela, Marek R},
  journal={Electronics},
  volume={12},
  number={9},
  pages={2056},
  year={2023},
  publisher={MDPI}
}

@inproceedings{windsor2022context,
  title={Context-aware transformers for spinal cancer detection and radiological grading},
  author={Windsor, Rhydian and Jamaludin, Amir and Kadir, Timor and Zisserman, Andrew},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={271--281},
  year={2022},
  organization={Springer}
}

% Self-supervised Learning
@article{devlin2018bert,
  title={Bert: Pre-training of deep bidirectional transformers for language understanding},
  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  journal={arXiv preprint arXiv:1810.04805},
  year={2018}
}

@inproceedings{he2022masked,
  title={Masked autoencoders are scalable vision learners},
  author={He, Kaiming and Chen, Xinlei and Xie, Saining and Li, Yanghao and Doll{\'a}r, Piotr and Girshick, Ross},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={16000--16009},
  year={2022}
}

@article{feichtenhofer2022masked,
  title={Masked autoencoders as spatiotemporal learners},
  author={Feichtenhofer, Christoph and Li, Yanghao and He, Kaiming and others},
  journal={Advances in neural information processing systems},
  volume={35},
  pages={35946--35958},
  year={2022}
}

@inproceedings{girdhar2023omnimae,
  title={Omnimae: Single model masked pretraining on images and videos},
  author={Girdhar, Rohit and El-Nouby, Alaaeldin and Singh, Mannat and Alwala, Kalyan Vasudev and Joulin, Armand and Misra, Ishan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={10406--10417},
  year={2023}
}

@inproceedings{assran2022masked,
  title={Masked siamese networks for label-efficient learning},
  author={Assran, Mahmoud and Caron, Mathilde and Misra, Ishan and Bojanowski, Piotr and Bordes, Florian and Vincent, Pascal and Joulin, Armand and Rabbat, Mike and Ballas, Nicolas},
  booktitle={European Conference on Computer Vision},
  pages={456--473},
  year={2022},
  organization={Springer}
}

@inproceedings{assran2023self,
  title={Self-supervised learning from images with a joint-embedding predictive architecture},
  author={Assran, Mahmoud and Duval, Quentin and Misra, Ishan and Bojanowski, Piotr and Vincent, Pascal and Rabbat, Michael and LeCun, Yann and Ballas, Nicolas},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={15619--15629},
  year={2023}
}

@article{zhou2021models,
  title={Models genesis},
  author={Zhou, Zongwei and Sodha, Vatsal and Pang, Jiaxuan and Gotway, Michael B and Liang, Jianming},
  journal={Medical image analysis},
  volume={67},
  pages={101840},
  year={2021},
  publisher={Elsevier}
}

@inproceedings{tang2022self,
  title={Self-supervised pre-training of swin transformers for 3d medical image analysis},
  author={Tang, Yucheng and Yang, Dong and Li, Wenqi and Roth, Holger R and Landman, Bennett and Xu, Daguang and Nath, Vishwesh and Hatamizadeh, Ali},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={20730--20740},
  year={2022}
}

% Video Pretraining
@inproceedings{deng2009imagenet,
  title={Imagenet: A large-scale hierarchical image database},
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
  booktitle={2009 IEEE conference on computer vision and pattern recognition},
  pages={248--255},
  year={2009},
  organization={Ieee}
}

@article{ke2023video,
  title={Video Pretraining Advances 3D Deep Learning on Chest CT Tasks},
  author={Ke, Alexander and Huang, Shih-Cheng and O'Connell, Chloe P and Klimont, Michal and Yeung, Serena and Rajpurkar, Pranav},
  journal={arXiv preprint arXiv:2304.00546},
  year={2023}
}

@article{rajpurkar2020appendixnet,
  title={AppendiXNet: deep learning for diagnosis of appendicitis from a small dataset of CT exams using video pretraining},
  author={Rajpurkar, Pranav and Park, Allison and Irvin, Jeremy and Chute, Chris and Bereket, Michael and Mastrodicasa, Domenico and Langlotz, Curtis P and Lungren, Matthew P and Ng, Andrew Y and Patel, Bhavik N},
  journal={Scientific reports},
  volume={10},
  number={1},
  pages={3958},
  year={2020},
  publisher={Nature Publishing Group UK London}
}

@article{zunair2021viptt,
  title={ViPTT-Net: Video pretraining of spatio-temporal model for tuberculosis type classification from chest CT scans},
  author={Zunair, Hasib and Rahman, Aimon and Mohammed, Nabeel},
  journal={arXiv preprint arXiv:2105.12810},
  year={2021}
}



% 3. Method
@article{carreira2019short,
  title={A short note on the kinetics-700 human action dataset},
  author={Carreira, Joao and Noland, Eric and Hillier, Chloe and Zisserman, Andrew},
  journal={arXiv preprint arXiv:1907.06987},
  year={2019}
}

@article{li2023transforming,
  title={Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives},
  author={Li, Jun and Chen, Junyu and Tang, Yucheng and Wang, Ce and Landman, Bennett A and Zhou, S Kevin},
  journal={Medical image analysis},
  pages={102762},
  year={2023},
  publisher={Elsevier}
}

% Positional Encoding Cropping
@inproceedings{kim2023region,
  title={Region-aware pretraining for open-vocabulary object detection with vision transformers},
  author={Kim, Dahun and Angelova, Anelia and Kuo, Weicheng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11144--11154},
  year={2023}
}

% Slice Sampling
@inproceedings{feichtenhofer2019slowfast,
  title={Slowfast networks for video recognition},
  author={Feichtenhofer, Christoph and Fan, Haoqi and Malik, Jitendra and He, Kaiming},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={6202--6211},
  year={2019}
}



% 4. Experimental Setup
% Preprocessing Pipeline
@inproceedings{burgin2023robust,
  title={Robust vertebra identification using simultaneous node and edge predicting Graph Neural Networks},
  author={B{\"u}rgin, Vincent and Prevost, Raphael and Stollenga, Marijn F},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={483--493},
  year={2023},
  organization={Springer}
}

% Unlabeled Vertebra Pretraining Dataset
@article{smith2015data,
  title={Data from CT\_COLONOGRAPHY},
  author={Smith, K and Clark, K and Bennett, W and Nolan, T and Kirby, J and Wolfsberger, M and Moulton, J and Vendt, B and Freymann, J},
  journal={Cancer Imaging Arch},
  volume={10},
  pages={K9},
  year={2015}
}

@article{an2020ct,
  title={Ct images in covid-19 [data set]},
  author={An, Peng and Xu, Sheng and Harmon, Stephanie A and Turkbey, Evrim B and Sanford, Thomas H and Amalou, Amel and Kassin, Michael and Varble, Nicole and Blain, Maxime and Anderson, Victoria and others},
  journal={The Cancer Imaging Archive},
  volume={10},
  year={2020}
}

@article{simpson2019large,
  title={A large annotated medical image dataset for the development and evaluation of segmentation algorithms},
  author={Simpson, Amber L and Antonelli, Michela and Bakas, Spyridon and Bilello, Michel and Farahani, Keyvan and Van Ginneken, Bram and Kopp-Schneider, Annette and Landman, Bennett A and Litjens, Geert and Menze, Bjoern and others},
  journal={arXiv preprint arXiv:1902.09063},
  year={2019}
}

@misc{bejarano2018head,
  title={Head-and-neck squamous cell carcinoma (HNSCC) patients with 3D CT taken during pre-treatment, mid-treatment, and post-treatment Dataset},
  author={Bejarano, Tatiana and Ornelas-Couto, Mariluz and Mihaylov, Ivaylo},
  year={2018},
  publisher={The Cancer Imaging Archive}
}

@article{yan2018deeplesion,
  title={DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning},
  author={Yan, Ke and Wang, Xiaosong and Lu, Le and Summers, Ronald M},
  journal={Journal of medical imaging},
  volume={5},
  number={3},
  pages={036501--036501},
  year={2018},
  publisher={Society of Photo-Optical Instrumentation Engineers}
}

@article{heller2021state,
  title={The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge},
  author={Heller, Nicholas and Isensee, Fabian and Maier-Hein, Klaus H and Hou, Xiaoshuai and Xie, Chunmei and Li, Fengyi and Nan, Yang and Mu, Guangrui and Lin, Zhiyong and Han, Miofei and others},
  journal={Medical image analysis},
  volume={67},
  pages={101821},
  year={2021},
  publisher={Elsevier}
}

@article{sekuboyina2021verse,
  title={VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images},
  author={Sekuboyina, Anjany and Husseini, Malek E and Bayat, Amirhossein and L{\"o}ffler, Maximilian and Liebl, Hans and Li, Hongwei and Tetteh, Giles and Kuka{\v{c}}ka, Jan and Payer, Christian and {\v{S}}tern, Darko and others},
  journal={Medical image analysis},
  volume={73},
  pages={102166},
  year={2021},
  publisher={Elsevier}
}

@article{deng2021ctspine1k,
  title={Ctspine1k: A large-scale dataset for spinal vertebrae segmentation in computed tomography},
  author={Deng, Yang and Wang, Ce and Hui, Yuan and Li, Qian and Li, Jun and Luo, Shiwei and Sun, Mengke and Quan, Quan and Yang, Shuxin and Hao, You and others},
  journal={arXiv preprint arXiv:2105.14711},
  year={2021}
}

% Labeled Vertebra Downstream Task Dataset
@article{foreman2024deep,
  title={Deep learning to differentiate benign and malignant vertebral fractures at multidetector CT},
  author={Foreman, Sarah C and Schinz, David and El Husseini, Malek and Goller, Sophia S and Wei{\ss}inger, J{\"u}rgen and Dietrich, Anna-Sophia and Renz, Martin and Metz, Marie-Christin and Feuerriegel, Georg C and Wiestler, Benedikt and others},
  journal={Radiology},
  volume={310},
  number={3},
  pages={e231429},
  year={2024},
  publisher={Radiological Society of North America}
}


% 4.2. Implementation Details
% Self-supervised Domain Adaptation Pretraining
@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}
}



% 5. Results and Discussion
% 5.2 Self-supervised Domain Adaptation
@article{setio2017validation,
  title={Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge},
  author={Setio, Arnaud Arindra Adiyoso and Traverso, Alberto and De Bel, Thomas and Berens, Moira SN and Van Den Bogaard, Cas and Cerello, Piergiorgio and Chen, Hao and Dou, Qi and Fantacci, Maria Evelina and Geurts, Bram and others},
  journal={Medical image analysis},
  volume={42},
  pages={1--13},
  year={2017},
  publisher={Elsevier}
}

@article{grossberg2020md,
  title={MD Anderson Cancer Center head and neck quantitative imaging working group},
  author={Grossberg, Aaron and Elhalawani, Hesham and Mohamed, Abdallah and Mulder, Sam and Williams, Bowman and White, Aubrey L and Zafereo, James and Wong, Andrew J and Berends, Joel E and AboHashem, Shady and others},
  journal={HNSCC (Dataset). The Cancer Imaging Archive},
  year={2020}
}

@article{armato2011lung,
  title={The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans},
  author={Armato III, Samuel G and McLennan, Geoffrey and Bidaut, Luc and McNitt-Gray, Michael F and Meyer, Charles R and Reeves, Anthony P and Zhao, Binsheng and Aberle, Denise R and Henschke, Claudia I and Hoffman, Eric A and others},
  journal={Medical physics},
  volume={38},
  number={2},
  pages={915--931},
  year={2011},
  publisher={Wiley Online Library}
}


% 5.3 Vertebra Fracture Detection
@inproceedings{huang2017densely,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4700--4708},
  year={2017}
}

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}



% Appendix
% Datasets - Prepocessing Pipeline
@article{clark2013cancer,
  title={The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository},
  author={Clark, Kenneth and Vendt, Bruce and Smith, Kirk and Freymann, John and Kirby, Justin and Koppel, Paul and Moore, Stephen and Phillips, Stanley and Maffitt, David and Pringle, Michael and others},
  journal={Journal of digital imaging},
  volume={26},
  pages={1045--1057},
  year={2013},
  publisher={Springer}
}

@article{johnson2008accuracy,
  title={Accuracy of CT colonography for detection of large adenomas and cancers},
  author={Johnson, C Daniel and Chen, Mei-Hsiu and Toledano, Alicia Y and Heiken, Jay P and Dachman, Abraham and Kuo, Mark D and Menias, Christine O and Siewert, Betina and Cheema, Jugesh I and Obregon, Richard G and others},
  journal={New England Journal of Medicine},
  volume={359},
  number={12},
  pages={1207--1217},
  year={2008},
  publisher={Mass Medical Soc}
}

@article{bejarano2019longitudinal,
  title={Longitudinal fan-beam computed tomography dataset for head-and-neck squamous cell carcinoma patients},
  author={Bejarano, Tatiana and De Ornelas-Couto, Mariluz and Mihaylov, Ivaylo B},
  journal={Medical physics},
  volume={46},
  number={5},
  pages={2526--2537},
  year={2019},
  publisher={Wiley Online Library}
}

@article{loffler2020vertebral,
  title={A vertebral segmentation dataset with fracture grading},
  author={L{\"o}ffler, Maximilian T and Sekuboyina, Anjany and Jacob, Alina and Grau, Anna-Lena and Scharr, Andreas and El Husseini, Malek and Kallweit, Mareike and Zimmer, Claus and Baum, Thomas and Kirschke, Jan S},
  journal={Radiology: Artificial Intelligence},
  volume={2},
  number={4},
  pages={e190138},
  year={2020},
  publisher={Radiological Society of North America}
}

@article{liebl2021computed,
  title={A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data},
  author={Liebl, Hans and Schinz, David and Sekuboyina, Anjany and Malagutti, Luca and L{\"o}ffler, Maximilian T and Bayat, Amirhossein and El Husseini, Malek and Tetteh, Giles and Grau, Katharina and Niederreiter, Eva and others},
  journal={Scientific data},
  volume={8},
  number={1},
  pages={284},
  year={2021},
  publisher={Nature Publishing Group UK London}
}