@inproceedings{rouge,
    title = "{{ROUGE}: A Package for Automatic Evaluation of Summaries}",
    author = "Lin, Chin-Yew",
    booktitle = "Text Summarization Branches Out",
    month = jul,
    year = "2004",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W04-1013",
    pages = "74--81"
}

@inproceedings{bertscore,
  author       = {Tianyi Zhang and
                  Varsha Kishore and
                  Felix Wu and
                  Kilian Q. Weinberger and
                  Yoav Artzi},
  title        = {{BERTScore: Evaluating Text Generation with {BERT}}},
  booktitle    = {8th International Conference on Learning Representations, {ICLR} 2020,
                  Addis Ababa, Ethiopia, April 26-30, 2020},
  publisher    = {OpenReview.net},
  year         = {2020},
  url          = {https://openreview.net/forum?id=SkeHuCVFDr}
}

@inproceedings{bleu,
  author       = {Kishore Papineni and
                  Salim Roukos and
                  Todd Ward and
                  Wei{-}Jing Zhu},
  title        = {{BLEU: a Method for Automatic Evaluation of Machine Translation}},
  booktitle    = {Proceedings of the 40th Annual Meeting of the Association for Computational
                  Linguistics, July 6-12, 2002, Philadelphia, PA, {USA}},
  pages        = {311--318},
  publisher    = {{ACL}},
  year         = {2002},
  url          = {https://aclanthology.org/P02-1040/}
}

@inproceedings{radeval,
  title={{RadEval: A framework for radiology text evaluation}},
  author={Xu, Justin and Zhang, Xi and Abderezaei, Javid and Bauml, Julie and Boodoo, Roger and Haghighi, Fatemeh and Ganjizadeh, Ali and Brattain, Eric and Van Veen, Dave and Meng, Zaiqiao and others},
  booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  pages={546--557},
  year={2025}
}

@inproceedings{textbrats,
  title={{TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module Exploration}},
  author={Shi, Xiaoyu and Jain, Rahul Kumar and Li, Yinhao and Hou, Ruibo and Cheng, Jingliang and Bai, Jie and Zhao, Guohua and Lin, Lanfen and Xu, Rui and Chen, Yen-wei},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={638--648},
  year={2025},
  organization={Springer}
}

@article{autorg,
  title={{Auto{RG}-{B}rain: {G}rounded Report Generation for Brain {MRI}}},
  author={Lei, Jiayu and Zhang, Xiaoman and Wu, Chaoyi and Dai, Lisong and Zhang, Ya and Zhang, Yanyong and Wang, Yanfeng and Xie, Weidi and Li, Yuehua},
  journal={arXiv preprint arXiv:2407.16684},
  year={2024}
}

@article{seg2exp,
  title={{From segmentation to explanation: Generating textual reports from MRI with LLMs}},
  author={Valerio, Alberto G and Trufanova, Katya and de Benedictis, Salvatore and Vessio, Gennaro and Castellano, Giovanna},
  journal={Computer Methods and Programs in Biomedicine},
  pages={108922},
  year={2025},
  publisher={Elsevier}
}
@article{mimiccxr,
  title        = {{MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports}},
  author       = {Johnson, Alistair E. W. and Pollard, Tom J. and Berkowitz, Seth J. and Greenbaum, Nathaniel R. and Lungren, Matthew P. and Deng, Chih-Ying and Mark, Roger G. and Horng, Steven},
  journal      = {Scientific Data},
  volume       = {6},
  pages        = {317},
  year         = {2019},
  doi          = {10.1038/s41597-019-0322-0},
  url          = {https://doi.org/10.1038/s41597-019-0322-0},
  publisher    = {Nature Publishing Group},
}


@inproceedings{cxr_reportgen,
  title={Clinically accurate chest x-ray report generation},
  author={Liu, Guanxiong and Hsu, Tzu-Ming Harry and McDermott, Matthew and Boag, Willie and Weng, Wei-Hung and Szolovits, Peter and Ghassemi, Marzyeh},
  booktitle={Machine Learning for Healthcare Conference},
  pages={249--269},
  year={2019},
  organization={PMLR}
}
@article{cxr_reportgen2,
  title={Generating radiology reports via memory-driven transformer},
  author={Chen, Zhihong and Song, Yan and Chang, Tsung-Hui and Wan, Xiang},
  journal={arXiv preprint arXiv:2010.16056},
  year={2020}
}
@article{sellergren2025medgemma,
  title={{MedGemma technical report}},
  author={Sellergren, Andrew and Kazemzadeh, Sahar and Jaroensri, Tiam and Kiraly, Atilla and Traverse, Madeleine and Kohlberger, Timo and Xu, Shawn and Jamil, Fayaz and Hughes, C{\'\i}an and Lau, Charles and others},
  journal={arXiv preprint arXiv:2507.05201},
  year={2025}
}

@article{synthmorph,
  title={{Anatomy-aware and acquisition-agnostic joint registration with SynthMorph}},
  author={Hoffmann, Malte and Hoopes, Andrew and Greve, Douglas N and Fischl, Bruce and Dalca, Adrian V},
  journal={Imaging Neuroscience},
  volume={2},
  pages={1--33},
  year={2024}
}

@inproceedings{bassi2025radgpt,
  title={{RadGPT: Constructing 3d image-text tumor datasets}},
  author={Bassi, Pedro RAS and Yavuz, Mehmet Can and Hamamci, Ibrahim Ethem and Er, Sezgin and Chen, Xiaoxi and Li, Wenxuan and Menze, Bjoern and Decherchi, Sergio and Cavalli, Andrea and Wang, Kang and others},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={23720--23730},
  year={2025}
}
@article{blondeel2025healthcare,
  title={{Healthcare Agent Orchestrator (HAO) for Patient Summarization in Molecular Tumor Boards}},
  author={Blondeel, Matthias and Codella, Noel and Preston, Sam and Qiu, Hao and Schettini, Leonardo and Tuan, Frank and Yim, Wen-wai and Saligrama, Smitha and {\"O}z, Mert and Jain, Shrey and others},
  journal={arXiv preprint arXiv:2509.06602},
  year={2025}
}
@article{vasari-auto,
  title={{VASARI-auto: Equitable, efficient, and economical featurisation of glioma MRI}},
  author={Ruffle, James K and Mohinta, Samia and Baruteau, Kelly Pegoretti and Rajiah, Rebekah and Lee, Faith and Brandner, Sebastian and Nachev, Parashkev and Hyare, Harpreet},
  journal={NeuroImage: Clinical},
  volume={44},
  pages={103668},
  year={2024},
  publisher={Elsevier}
}


@article{jain2014outcome,
  title={{Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor}},
  author={Jain, Rajan and Poisson, Laila M and Gutman, David and Scarpace, Lisa and Hwang, Scott N and Holder, Chad A and Wintermark, Max and Rao, Arvind and Colen, Rivka R and Kirby, Justin and others},
  journal={Radiology},
  volume={272},
  number={2},
  pages={484--493},
  year={2014},
  publisher={Radiological Society of North America}
}

@article{nicolasjilwan2015addition,
  title={{Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients}},
  author={Nicolasjilwan, Manal and Hu, Ying and Yan, Chunhua and Meerzaman, Daoud and Holder, Chad A and Gutman, David and Jain, Rajan and Colen, Rivka and Rubin, Daniel L and Zinn, Pascal O and others},
  journal={Journal of Neuroradiology},
  volume={42},
  number={4},
  pages={212--221},
  year={2015},
  publisher={Elsevier}
}

@article{peeken2019combining,
  title={{Combining multimodal imaging and treatment features improves machine learning-based prognostic assessment in patients with glioblastoma multiforme}},
  author={Peeken, Jan C and Goldberg, Tatyana and Pyka, Thomas and Bernhofer, Michael and Wiestler, Benedikt and Kessel, Kerstin A and Tafti, Pouya D and N{\"u}sslin, Fridtjof and Braun, Andreas E and Zimmer, Claus and others},
  journal={Cancer medicine},
  volume={8},
  number={1},
  pages={128--136},
  year={2019},
  publisher={Wiley Online Library}
}

@article{setyawan2024beyond,
  title={{Beyond invasive biopsies: using VASARI MRI features to predict grade and molecular parameters in gliomas}},
  author={Setyawan, Nurhuda Hendra and Choridah, Lina and Nugroho, Hanung Adi and Malueka, Rusdy Ghazali and Dwianingsih, Ery Kus},
  journal={Cancer Imaging},
  volume={24},
  number={1},
  pages={3},
  year={2024},
  publisher={Springer}
}

@article{wang2021preoperative,
  title={{Preoperative magnetic resonance imaging radiomics for predicting early recurrence of glioblastoma}},
  author={Wang, Jing and Yi, Xiaoping and Fu, Yan and Pang, Peipei and Deng, Huihuang and Tang, Haiyun and Han, Zaide and Li, Haiping and Nie, Jilin and Gong, Guanghui and others},
  journal={Frontiers in Oncology},
  volume={11},
  pages={769188},
  year={2021},
  publisher={Frontiers Media SA}
}
@article{zhou2017mri,
  title={{MRI features predict survival and molecular markers in diffuse lower-grade gliomas}},
  author={Zhou, Hao and Valli{\`e}res, Martin and Bai, Harrison X and Su, Chang and Tang, Haiyun and Oldridge, Derek and Zhang, Zishu and Xiao, Bo and Liao, Weihua and Tao, Yongguang and others},
  journal={Neuro-oncology},
  volume={19},
  number={6},
  pages={862--870},
  year={2017},
  publisher={Oxford University Press US}
}

@article{brats24,
  title={{The 2024 brain tumor segmentation (brats) challenge: Glioma segmentation on post-treatment MRI}},
  author={de Verdier, Maria Correia and Saluja, Rachit and Gagnon, Louis and LaBella, Dominic and Baid, Ujjwall and Tahon, Nourel Hoda and Foltyn-Dumitru, Martha and Zhang, Jikai and Alafif, Maram and Baig, Saif and others},
  journal={arXiv preprint arXiv:2405.18368},
  year={2024}
}

@article{iuxray,
  title = {{Preparing a collection of radiology examinations for distribution and retrieval}},
  author = {Demner-Fushman, Dina and Kohli, Marc D. and Rosenman, Marc B. and Shooshan, Sonya E. and Rodriguez, Laritza and Antani, Sameer and Thoma, George R. and McDonald, Clement J.},
  journal = {Journal of the American Medical Informatics Association},
  volume = {23},
  number = {2},
  pages = {304--310},
  year = {2016},
  month = {March},
  doi = {10.1093/jamia/ocv080}
}

@article{christensen2025projected,
  title={{Projected US radiologist supply, 2025 to 2055}},
  author={Christensen, Eric W and Parikh, Jay R and Drake, Alexandra R and Rubin, Eric M and Rula, Elizabeth Y},
  journal={Journal of the American College of Radiology},
  volume={22},
  number={2},
  pages={161--169},
  year={2025},
  publisher={Elsevier}
}

@article{wu2025first,
  title={{First, do NOHARM: towards clinically safe large language models}},
  author={Wu, David and Haredasht, Fateme Nateghi and Maharaj, Saloni Kumar and Jain, Priyank and Tran, Jessica and Gwiazdon, Matthew and Rustagi, Arjun and Jindal, Jenelle and Koshy, Jacob M and Kadiyala, Vinay and others},
  journal={arXiv preprint arXiv:2512.01241},
  year={2025}
}

@article{medpalm2,
  title={Large language models encode clinical knowledge},
  author={Singhal, Karan and Azizi, Shekoofeh and Tu, Tao and Mahdavi, S Sara and Wei, Jason and Chung, Hyung Won and Scales, Nathan and Tanwani, Ajay and Cole-Lewis, Heather and Pfohl, Stephen and others},
  journal={Nature},
  volume={620},
  number={7972},
  pages={172--180},
  year={2023},
  publisher={Nature Publishing Group}
}

@article{achiam2023gpt,
  title={{GPT-4 technical report}},
  author={Achiam, Josh and Adler, Steven and Agarwal, Sandhini and Ahmad, Lama and Akkaya, Ilge and Aleman, Florencia Leoni and Almeida, Diogo and Altenschmidt, Janko and Altman, Sam and Anadkat, Shyamal and others},
  journal={arXiv preprint arXiv:2303.08774},
  year={2023}
}

@article{ostrom2014,
    author = {Ostrom, Quinn T. and Bauchet, Luc and Davis, Faith G. and Deltour, Isabelle and Fisher, James L. and Langer, Chelsea Eastman and Pekmezci, Melike and Schwartzbaum, Judith A. and Turner, Michelle C. and Walsh, Kyle M. and Wrensch, Margaret R. and Barnholtz-Sloan, Jill S.},
    title = {{The epidemiology of glioma in adults: a “state of the science” review}},
    journal = {Neuro-Oncology},
    volume = {16},
    number = {7},
    pages = {896-913},
    year = {2014},
    month = {05},
    abstract = {Gliomas are the most common primary intracranial tumor, representing 81\% of malignant brain tumors. Although relatively rare, they cause significant mortality and morbidity. Glioblastoma, the most common glioma histology (∼45\% of all gliomas), has a 5-year relative survival of ∼5\%. A small portion of these tumors are caused by Mendelian disorders, including neurofibromatosis, tuberous sclerosis, and Li-Fraumeni syndrome. Genomic analyses of glioma have also produced new evidence about risk and prognosis. Recently discovered biomarkers that indicate improved survival include O6-methylguanine-DNA methyltransferase methylation, isocitrate dehydrogenase mutation, and a glioma cytosine–phosphate–guanine island methylator phenotype. Genome-wide association studies have identified heritable risk alleles within 7 genes that are associated with increased risk of glioma. Many risk factors have been examined as potential contributors to glioma risk. Most significantly, these include an increase in risk by exposure to ionizing radiation and a decrease in risk by history of allergies or atopic disease(s). The potential influence of occupational exposures and cellular phones has also been examined, with inconclusive results. We provide a “state of the science” review of current research into causes and risk factors for gliomas in adults.},
    issn = {1522-8517},
    doi = {10.1093/neuonc/nou087},
    url = {https://doi.org/10.1093/neuonc/nou087},
    eprint = {https://academic.oup.com/neuro-oncology/article-pdf/16/7/896/16665942/nou087.pdf},
}

@article{ostrom2017,
    author = {Thakkar, Jigisha P. and Dolecek, Therese A. and Horbinski, Craig and Ostrom, Quinn T. and Lightner, Donita D. and Barnholtz-Sloan, Jill S. and Villano, John L.},
    title = {{Epidemiologic and Molecular Prognostic Review of Glioblastoma}},
    journal = {Cancer Epidemiology, Biomarkers \& Prevention},
    volume = {23},
    number = {10},
    pages = {1985-1996},
    year = {2014},
    month = {09},
    abstract = {Glioblastoma multiforme (GBM) is the most common and aggressive primary central nervous system malignancy with a median survival of 15 months. The average incidence rate of GBM is 3.19/100,000 population, and the median age of diagnosis is 64 years. Incidence is higher in men and individuals of white race and non-Hispanic ethnicity. Many genetic and environmental factors have been studied in GBM, but the majority are sporadic, and no risk factor accounting for a large proportion of GBMs has been identified. However, several favorable clinical prognostic factors are identified, including younger age at diagnosis, cerebellar location, high performance status, and maximal tumor resection. GBMs comprise of primary and secondary subtypes, which evolve through different genetic pathways, affect patients at different ages, and have differences in outcomes. We report the current epidemiology of GBM with new data from the Central Brain Tumor Registry of the United States 2006 to 2010 as well as demonstrate and discuss trends in incidence and survival. We also provide a concise review on molecular markers in GBM that have helped distinguish biologically similar subtypes of GBM and have prognostic and predictive value. Cancer Epidemiol Biomarkers Prev; 23(10); 1985–96. ©2014 AACR.},
    issn = {1055-9965},
    doi = {10.1158/1055-9965.EPI-14-0275},
    url = {https://doi.org/10.1158/1055-9965.EPI-14-0275},
    eprint = {https://aacrjournals.org/cebp/article-pdf/23/10/1985/2278434/1985.pdf},
}

@article{bi2014,
    author = {Bi, Wenya Linda and Beroukhim, Rameen},
    title = {Beating the odds: extreme long-term survival with glioblastoma},
    journal = {Neuro-Oncology},
    volume = {16},
    number = {9},
    pages = {1159-1160},
    year = {2014},
    month = {09},
    abstract = {See the article by Gerber et al, on pages 1186–1195.},
    issn = {1522-8517},
    doi = {10.1093/neuonc/nou166},
    url = {https://doi.org/10.1093/neuonc/nou166},
    eprint = {https://academic.oup.com/neuro-oncology/article-pdf/16/9/1159/7640685/nou166.pdf},
}

@article{menze2014multimodal,
  title={{The multimodal brain tumor image segmentation benchmark (BRATS)}},
  author={Menze, Bjoern H and Jakab, Andras and Bauer, Stefan and Kalpathy-Cramer, Jayashree and Farahani, Keyvan and Kirby, Justin and Burren, Yuliya and Porz, Nicole and Slotboom, Johannes and Wiest, Roland and others},
  journal={IEEE transactions on medical imaging},
  volume={34},
  number={10},
  pages={1993--2024},
  year={2014},
  publisher={IEEE}
}
@article{deepmedic,
  title={{Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation}},
  author={Kamnitsas, Konstantinos and Ledig, Christian and Newcombe, Virginia FJ and Simpson, Joanna P and Kane, Andrew D and Menon, David K and Rueckert, Daniel and Glocker, Ben},
  journal={Medical image analysis},
  volume={36},
  pages={61--78},
  year={2017},
  publisher={Elsevier}
}

@article{UCSF-PDGM,
  author       = {Calabrese, E. and Villanueva-Meyer, J. and Rudie, J. and Rauschecker, A. and Baid, U. and Bakas, S. and Cha, S. and Mongan, J. and Hess, C.},
  title        = {{The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM)}},
  year         = {2022},
  version      = {5},
  howpublished = {\url{https://doi.org/10.7937/tcia.bdgf-8v37}},
  note         = {Dataset on The Cancer Imaging Archive (TCIA)}
}

@article{UPenn-GBM,
  author       = {Bakas, S. and Sako, C. and Akbari, H. and Bilello, M. and Sotiras, A. and Shukla, G. and Rudie, J. D. and Flores Santamaria, N. and Fathi Kazerooni, A. and Pati, S. and Rathore, S. and Mamourian, E. and Ha, S. M. and Parker, W. and Doshi, J. and Baid, U. and Bergman, M. and Binder, Z. A. and Verma, R. and Davatzikos, C.},
  title        = {{Multi-parametric magnetic resonance imaging (mpMRI) scans for de novo Glioblastoma (GBM) patients from the University of Pennsylvania Health System (UPENN-GBM)}},
  year         = {2021},
  version      = {2},
  howpublished = {\url{https://doi.org/10.7937/TCIA.709X-DN49}},
  note         = {Dataset on The Cancer Imaging Archive (TCIA)}
}

@article{CPTAC-GBM,
  author       = {{National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC)}},
  title        = {{The Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multiforme Collection (CPTAC-GBM)}},
  year         = {2018},
  version      = {16},
  howpublished = {\url{https://doi.org/10.7937/K9/TCIA.2018.3RJE41Q1}},
  note         = {Dataset on The Cancer Imaging Archive (TCIA)}
}

@misc{TCGA-LGG,
  author       = {Pedano, N. and Flanders, A. E. and Scarpace, L. and Mikkelsen, T. and Eschbacher, J. M. and Hermes, B. and Sisneros, V. and Barnholtz-Sloan, J. and Ostrom, Q.},
  title        = {{The Cancer Genome Atlas Low Grade Glioma Collection (TCGA-LGG)}},
  year         = {2016},
  version      = {3},
  howpublished = {\url{https://doi.org/10.7937/K9/TCIA.2016.L4LTD3TK}},
  note         = {Dataset on The Cancer Imaging Archive (TCIA)}
}

@misc{TCGA-GBM,
  author       = {Scarpace, L. and Mikkelsen, T. and Cha, S. and Rao, S. and Tekchandani, S. and Gutman, D. and Saltz, J. H. and Erickson, B. J. and Pedano, N. and Flanders, A. E. and Barnholtz-Sloan, J. and Ostrom, Q. and Barboriak, D. and Pierce, L. J.},
  title        = {{The Cancer Genome Atlas Glioblastoma Multiforme Collection (TCGA-GBM)}},
  year         = {2016},
  version      = {5},
  howpublished = {\url{https://doi.org/10.7937/K9/TCIA.2016.RNYFUYE9}},
  note         = {Dataset on The Cancer Imaging Archive (TCIA)}
}

@article{brats23,
  title={{The brain tumor segmentation (BraTS) challenge 2023: Glioma segmentation in sub-saharan Africa patient population (BraTS-Africa)}},
  author={Adewole, Maruf and Rudie, Jeffrey D and Gbdamosi, Anu and Toyobo, Oluyemisi and Raymond, Confidence and Zhang, Dong and Omidiji, Olubukola and Akinola, Rachel and Suwaid, Mohammad Abba and Emegoakor, Adaobi and others},
  journal={ArXiv},
  pages={arXiv--2305},
  year={2023}
}

@article{lifelines,
  author       = {Davidson-Pilon, Cameron},
  title        = {{lifelines: Survival Analysis in Python}},
  year         = {2024},
  version      = {0.30.0},
  howpublished = {\url{https://doi.org/10.5281/zenodo.14007206}},
  note         = {Zenodo software release}
}

@article{grattafiori2024llama,
  title={{The LLaMa 3 herd of models}},
  author={Grattafiori, Aaron and Dubey, Abhimanyu and Jauhri, Abhinav and Pandey, Abhinav and Kadian, Abhishek and Al-Dahle, Ahmad and Letman, Aiesha and Mathur, Akhil and Schelten, Alan and Vaughan, Alex and others},
  journal={arXiv preprint arXiv:2407.21783},
  year={2024}
}
@article{gptoss,
  title={gpt-oss-120b \& gpt-oss-20b model card},
  author={Agarwal, Sandhini and Ahmad, Lama and Ai, Jason and Altman, Sam and Applebaum, Andy and Arbus, Edwin and Arora, Rahul K and Bai, Yu and Baker, Bowen and Bao, Haiming and others},
  journal={arXiv preprint arXiv:2508.10925},
  year={2025}
}



@article{team2025gemma,
  title={{Gemma 3 technical report}},
  author={{Gemma Team} and Kamath, Aishwarya and Ferret, Johan and Pathak, Shreya
          and Vieillard, Nino and Merhej, Ramona and Perrin, Sarah and Matejovicova, Tatiana
          and Ramé, Alexandre and Rivière, Morgane},
  journal={arXiv preprint arXiv:2503.19786},
  year={2025}
}


@article{Davatzikos2018CIPT,
  author       = {C. Davatzikos and S. Rathore and S. Bakas and S. Pati and M. Bergman and R. Kalarot and P. Sridharan and A. Gastounioti and N. Jahani and E. Cohen and H. Akbari and B. Tunc and J. Doshi and D. Parker and M. Hsieh and A. Sotiras and H. Li and Y. Ou and R. K. Doot and M. Bilello and Y. Fan and R. T. Shinohara and P. Yushkevich and R. Verma and D. Kontos},
  title        = {{Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome}},
  journal      = {Journal of Medical Imaging},
  volume       = {5},
  number       = {1},
  pages        = {011018},
  year         = {2018},
  doi          = {10.1117/1.JMI.5.1.011018}
}

@inproceedings{Pati2020CaPTk,
  author       = {S. Pati and A. Singh and S. Rathore and A. Gastounioti and M. Bergman and P. Ngo and S. M. Ha and D. Bounias and J. Minock and G. Murphy and H. Li and A. Bhattarai and A. Wolf and P. Sridaran and R. Kalarot and H. Akbari and A. Sotiras and S. P. Thakur and R. Verma and R. T. Shinohara and P. Yushkevich and Y. Fan and D. Kontos and C. Davatzikos and S. Bakas},
  title        = {{The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview}},
  booktitle    = {BrainLes 2019. Lecture Notes in Computer Science},
  volume       = {11993},
  pages        = {380--394},
  publisher    = {Springer},
  year         = {2020},
  doi          = {10.1007/978-3-030-46643-5\_38}
}

@article{mnifonov2011unbiased,
  title={Unbiased average age-appropriate atlases for pediatric studies},
  author={Fonov, Vladimir and Evans, Alan C and Botteron, Kelly and Almli, C Robert and McKinstry, Robert C and Collins, D Louis and Brain Development Cooperative Group and others},
  journal={NeuroImage},
  volume={54},
  number={1},
  pages={313--327},
  year={2011},
  publisher={Elsevier}
}

@article{mnifonov2009unbiased,
  title={Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
  author={Fonov, Vladimir S and Evans, Alan C and McKinstry, Robert C and Almli, CR and Collins, DL},
  journal={NeuroImage},
  volume={47},
  pages={S102},
  year={2009},
  publisher={Academic Press}
}

@inproceedings{mnicollins1999animal,
  title={{ANIMAL+ INSECT: improved cortical structure segmentation}},
  author={Collins, D Louis and Zijdenbos, Alex P and Baar{\'e}, Wim FC and Evans, Alan C},
  booktitle={Biennial International Conference on Information Processing in Medical Imaging},
  pages={210--223},
  year={1999},
  organization={Springer}
}

@inproceedings{zhao2024ratescore,
  title={{RaTEScore: A Metric for Radiology Report Generation}},
  author={Zhao, Weike and Wu, Chaoyi and Zhang, Xiaoman and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
  booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  pages={15004--15019},
  year={2024}
}

@article{billot2023synthseg,
  title={{SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining}},
  author={Billot, Benjamin and Greve, Douglas N and Puonti, Oula and Thielscher, Axel and Van Leemput, Koen and Fischl, Bruce and Dalca, Adrian V and Iglesias, Juan Eugenio and others},
  journal={Medical image analysis},
  volume={86},
  pages={102789},
  year={2023},
  publisher={Elsevier}
}

@article{guo2025deepseek,
  title={Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning},
  author={Guo, Daya and Yang, Dejian and Zhang, Haowei and Song, Junxiao and Zhang, Ruoyu and Xu, Runxin and Zhu, Qihao and Ma, Shirong and Wang, Peiyi and Bi, Xiao and others},
  journal={arXiv preprint arXiv:2501.12948},
  year={2025}
}



@inproceedings{reimers-2019-sentence-bert,
  title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
  author = "Reimers, Nils and Gurevych, Iryna",
  booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
  month = "11",
  year = "2019",
  publisher = "Association for Computational Linguistics",
  url = "https://arxiv.org/abs/1908.10084",
}


@inproceedings{chen2024spatialvlm,
  title={Spatialvlm: Endowing vision-language models with spatial reasoning capabilities},
  author={Chen, Boyuan and Xu, Zhuo and Kirmani, Sean and Ichter, Brain and Sadigh, Dorsa and Guibas, Leonidas and Xia, Fei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14455--14465},
  year={2024}
}

@article{iglesias2023synthsr,
  title={SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry},
  author={Iglesias, Juan E and Billot, Benjamin and Balbastre, Ya{\"e}l and Magdamo, Colin and Arnold, Steven E and Das, Sudeshna and Edlow, Brian L and Alexander, Daniel C and Golland, Polina and Fischl, Bruce},
  journal={Science advances},
  volume={9},
  number={5},
  pages={eadd3607},
  year={2023},
  publisher={American Association for the Advancement of Science}
}

@misc{Rorden2024Niivue,
  author       = {Rorden, Chris and others},
  title        = {{NiiVue: WebGL2 based medical image viewer}},
  year         = {2024},
  howpublished = {\url{https://github.com/niivue/niivue}},
  note         = {GitHub repository}
}


@software{goel2025langextract,
  author  = {Goel, A.},
  title   = {{LangExtract (1.0.9)}},
  year    = {2025},
  publisher = {Zenodo},
  doi     = {10.5281/zenodo.17015090},
  url     = {https://doi.org/10.5281/zenodo.17015090}
}


@article{zanolini2022midline,
  title={Midline shift in chronic subdural hematoma: interrater reliability of different measuring methods and implications for standardized rating in embolization trials},
  author={Zanolini, Umberto and Austein, Friederike and Fiehler, Jens and McDonough, Rosalie and Rai, Hamid and Siddiqui, Adnan and Shotar, Eimad and Rouchaud, Aymeric and Goyal, Mayank and Kallmes, Kevin and others},
  journal={Clinical Neuroradiology},
  volume={32},
  number={4},
  pages={931--938},
  year={2022},
  publisher={Springer}
}

@article{ferreira2024we,
  title={How we won brats 2023 adult glioma challenge? just faking it! enhanced synthetic data augmentation and model ensemble for brain tumour segmentation},
  author={Ferreira, Andr{\'e} and Solak, Naida and Li, Jianning and Dammann, Philipp and Kleesiek, Jens and Alves, Victor and Egger, Jan},
  journal={arXiv preprint arXiv:2402.17317},
  year={2024}
}

@article{isensee2021nnunet,
  title   = {{nnU-Net: A Self-Configuring Method for Deep Learning-Based Biomedical Image Segmentation}},
  author  = {Isensee, Fabian and Jaeger, Paul F. and Kohl, Simon A. A. and Petersen, Jens and Maier-Hein, Klaus H.},
  journal = {Nature Methods},
  volume  = {18},
  number  = {2},
  pages   = {203--211},
  year    = {2021},
  doi     = {10.1038/s41592-020-01008-z}
}
@article{pemberton2023multi,
  title={Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms},
  author={Pemberton, Hugh G and Wu, Jiaming and Kommers, Ivar and M{\"u}ller, Domenique MJ and Hu, Yipeng and Goodkin, Olivia and Vos, Sjoerd B and Bisdas, Sotirios and Robe, Pierre A and Ardon, Hilko and others},
  journal={Scientific reports},
  volume={13},
  number={1},
  pages={18911},
  year={2023},
  publisher={Nature Publishing Group UK London}
}