@article{MAURO225101571,
        title = {Hepatocellular carcinoma: Epidemiology, diagnosis and treatment},
        journal = {JHEP Reports},
        volume = {7},
        number = {12},
        pages = {101571},
        year = {2025},
        issn = {2589-5559},
        doi = {https://doi.org/10.1016/j.jhepr.2025.101571},
        url = {https://www.sciencedirect.com/science/article/pii/S2589555925002538},
        author = {Ezequiel Mauro and Tiago {de Castro} and Marcus Zeitlhoefler and Max W. Sung and Augusto Villanueva and Vincenzo Mazzaferro and Josep M. Llovet},
        keywords = {hepatocellular carcinoma, epidemiology, staging, treatment},
        abstract = {Summary
        Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide and primarily affects individuals with cirrhosis. While viral hepatitis has historically been the predominant cause, the burden of HCC is increasingly shifting toward non-viral aetiologies, such as ALD (alcohol-related liver disease) and MASLD (metabolic dysfunction-associated steatotic liver disease). Preventive strategies, including HBV vaccination and antiviral therapies, have reduced the incidence of virus-related HCC, while emerging antifibrotic and metabolic agents may limit MASLD progression. Surveillance with biannual ultrasound and alpha-fetoprotein testing facilitates early detection and curative treatment, although underuse remains a critical limitation. Advances in surgical resection, liver transplantation, and locoregional therapies have improved outcomes in early and intermediate stages. In advanced HCC, immune checkpoint inhibitor-based combinations have shifted the treatment landscape, with median overall survival now exceeding two years and long-term responses observed in a subset of patients. Despite these advances, substantial unmet needs remain in prevention, early detection, and therapeutic access. Ongoing research into biomarkers and novel treatments offers the potential to further transform the HCC care continuum.}
}

@misc{blankemeier2024merlinvisionlanguagefoundation,
      title={Merlin: A Vision Language Foundation Model for 3D Computed Tomography}, 
      author={Louis Blankemeier and Joseph Paul Cohen and Ashwin Kumar and Dave Van Veen and Syed Jamal Safdar Gardezi and Magdalini Paschali and Zhihong Chen and Jean-Benoit Delbrouck and Eduardo Reis and Cesar Truyts and Christian Bluethgen and Malte Engmann Kjeldskov Jensen and Sophie Ostmeier and Maya Varma and Jeya Maria Jose Valanarasu and Zhongnan Fang and Zepeng Huo and Zaid Nabulsi and Diego Ardila and Wei-Hung Weng and Edson Amaro Junior and Neera Ahuja and Jason Fries and Nigam H. Shah and Andrew Johnston and Robert D. Boutin and Andrew Wentland and Curtis P. Langlotz and Jason Hom and Sergios Gatidis and Akshay S. Chaudhari},
      year={2024},
      eprint={2406.06512},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2406.06512}, 
}

@Article{Singal2023,
author={Singal, Amit G.
and Kanwal, Fasiha
and Llovet, Josep M.},
title={Global trends in hepatocellular carcinoma epidemiology: implications for screening, prevention and therapy},
journal={Nature Reviews Clinical Oncology},
year={2023},
month={Dec},
day={01},
volume={20},
number={12},
pages={864-884},
abstract={Hepatocellular carcinoma (HCC) mortality rates are increasing globally, and particularly in the Western world. Cirrhosis remains the predominant risk factor for HCC. However, epidemiological shifts in the incidence of HCC from patients with virus-related liver disease to those with non-viral aetiologies, including alcohol-associated and metabolic dysfunction-associated steatotic liver disease, have important implications for prevention, surveillance and treatment. Hepatitis B vaccination and antiviral therapy for hepatitis B and C are effective for primary prevention of virus-related HCCs, but chemoprevention strategies for non-viral liver disease remain an unmet need. Emerging data suggest associations between aspirin, statins, metformin and coffee and reduced HCC incidence, although none has been proved to be causally related. Secondary prevention of HCC via semi-annual surveillance is associated with improvements in early detection and thus reduced mortality; however, current tools, including abdominal ultrasonography, have suboptimal sensitivity for the detection of early stage HCC, particularly in patients with obesity and/or non-viral liver disease. Promising blood-based or imaging-based surveillance strategies are emerging, although these approaches require further validation before adoption in clinical practice. In the interim, efforts should be focused on maximizing use of the existing surveillance tools given their prevalent underuse globally. Remarkable advances have been made in the treatment of HCC, including expanded eligibility for surgical therapies, improved patient selection for locoregional treatments and increased systemic treatment options, including immune-checkpoint inhibitors. In this Review, we discuss trends in the epidemiology of HCC and their implications for screening, prevention and therapy.},
issn={1759-4782},
doi={10.1038/s41571-023-00825-3},
url={https://doi.org/10.1038/s41571-023-00825-3}
}

@article{https://doi.org/10.3322/caac.21834,
author = {Bray, Freddie and Laversanne, Mathieu and Sung, Hyuna and Ferlay, Jacques and Siegel, Rebecca L. and Soerjomataram, Isabelle and Jemal, Ahmedin},
title = {Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries},
journal = {CA: A Cancer Journal for Clinicians},
volume = {74},
number = {3},
pages = {229-263},
keywords = {cancer burden, cancer control, epidemiology, incidence, mortality},
doi = {https://doi.org/10.3322/caac.21834},
url = {https://acsjournals.onlinelibrary.wiley.com/doi/abs/10.3322/caac.21834},
eprint = {https://acsjournals.onlinelibrary.wiley.com/doi/pdf/10.3322/caac.21834},
abstract = {Abstract This article presents global cancer statistics by world region for the year 2022 based on updated estimates from the International Agency for Research on Cancer (IARC). There were close to 20 million new cases of cancer in the year 2022 (including nonmelanoma skin cancers [NMSCs]) alongside 9.7 million deaths from cancer (including NMSC). The estimates suggest that approximately one in five men or women develop cancer in a lifetime, whereas around one in nine men and one in 12 women die from it. Lung cancer was the most frequently diagnosed cancer in 2022, responsible for almost 2.5 million new cases, or one in eight cancers worldwide (12.4\% of all cancers globally), followed by cancers of the female breast (11.6\%), colorectum (9.6\%), prostate (7.3\%), and stomach (4.9\%). Lung cancer was also the leading cause of cancer death, with an estimated 1.8 million deaths (18.7\%), followed by colorectal (9.3\%), liver (7.8\%), female breast (6.9\%), and stomach (6.8\%) cancers. Breast cancer and lung cancer were the most frequent cancers in women and men, respectively (both cases and deaths). Incidence rates (including NMSC) varied from four-fold to five-fold across world regions, from over 500 in Australia/New Zealand (507.9 per 100,000) to under 100 in Western Africa (97.1 per 100,000) among men, and from over 400 in Australia/New Zealand (410.5 per 100,000) to close to 100 in South-Central Asia (103.3 per 100,000) among women. The authors examine the geographic variability across 20 world regions for the 10 leading cancer types, discussing recent trends, the underlying determinants, and the prospects for global cancer prevention and control. With demographics-based predictions indicating that the number of new cases of cancer will reach 35 million by 2050, investments in prevention, including the targeting of key risk factors for cancer (including smoking, overweight and obesity, and infection), could avert millions of future cancer diagnoses and save many lives worldwide, bringing huge economic as well as societal dividends to countries over the forthcoming decades.},
year = {2024}
}

@misc{aleem2024convloraadabnbaseddomain,
      title={ConvLoRA and AdaBN based Domain Adaptation via Self-Training}, 
      author={Sidra Aleem and Julia Dietlmeier and Eric Arazo and Suzanne Little},
      year={2024},
      eprint={2402.04964},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2402.04964}, 
}

@misc{radford2021learningtransferablevisualmodels,
      title={Learning Transferable Visual Models From Natural Language Supervision}, 
      author={Alec Radford and Jong Wook Kim and Chris Hallacy and Aditya Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
      year={2021},
      eprint={2103.00020},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2103.00020}, 
}

@misc{hu2021loralowrankadaptationlarge,
      title={LoRA: Low-Rank Adaptation of Large Language Models}, 
      author={Edward J. Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen},
      year={2021},
      eprint={2106.09685},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2106.09685}, 
}

@article{RUMGAY2022108,
title = {Global, regional and national burden of primary liver cancer by subtype},
journal = {European Journal of Cancer},
volume = {161},
pages = {108-118},
year = {2022},
issn = {0959-8049},
doi = {https://doi.org/10.1016/j.ejca.2021.11.023},
url = {https://www.sciencedirect.com/science/article/pii/S0959804921012430},
author = {Harriet Rumgay and Jacques Ferlay and Catherine {de Martel} and Damien Georges and Amal Samy Ibrahim and Rongshou Zheng and Wenqiang Wei and Valery E.P.P. Lemmens and Isabelle Soerjomataram},
keywords = {Liver cancer, Hepatocellular carcinoma, Cholangiocarcinoma, Incidence, Global},
abstract = {Introduction
Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) are the two main histological subtypes of primary liver cancer. Estimates of the burden of liver cancer by subtype are needed to facilitate development and evaluation of liver cancer control globally. We provide worldwide, regional and national estimates of HCC and iCCA incidence using high-quality data.
Methods
We used population-based cancer registry data on liver cancer cases by histological subtype from 95 countries to compute the sex- and country-specific distributions of HCC, iCCA and other specified histology. Subtype distributions were applied to estimates of total liver cancer cases for 2018 from the Global Cancer Observatory. Age-standardised incidence rates (ASRs) were calculated.
Results
There were an estimated 826,000 cases of liver cancer globally in 2018: 661,000 HCC (ASR 7.3 cases per 100,000); 123,000 iCCA (ASR 1.4) and 42,000 other specified histology (ASR 0.5). HCC contributed 80% of the world total liver cancer burden followed by iCCA (14.9%) and other specified histology (5.1%). HCC rates were highest in Eastern Asia (ASR 14.8), Northern Africa (ASR 13.2) and South-Eastern Asia (ASR 9.5). Rates of iCCA were highest in South-Eastern Asia (ASR 2.9), Eastern Asia (ASR 2.0), Northern Europe, the Caribbean and Central America and Oceania (ASR all 1.8).
Conclusion
We have shown the importance of uncovering the distinct patterns of the major subtypes of liver cancer. The use of these estimates is critical to further develop public health policy to reduce the burden of liver cancer and monitor progress in controlling HCC and iCCA globally.}
}


@InProceedings{pmlr-v182-zhang22a,
  title = 	 {Contrastive Learning of Medical Visual Representations from Paired Images and Text},
  author =       {Zhang, Yuhao and Jiang, Hang and Miura, Yasuhide and Manning, Christopher D. and Langlotz, Curtis P.},
  booktitle = 	 {Proceedings of the 7th Machine Learning for Healthcare Conference},
  pages = 	 {2--25},
  year = 	 {2022},
  editor = 	 {Lipton, Zachary and Ranganath, Rajesh and Sendak, Mark and Sjoding, Michael and Yeung, Serena},
  volume = 	 {182},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {05--06 Aug},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v182/zhang22a/zhang22a.pdf},
  url = 	 {https://proceedings.mlr.press/v182/zhang22a.html},
  abstract = 	 {Learning visual representations of medical images (e.g., X-rays) is core to medical image understanding but its progress has been held back by the scarcity of human annotations. Existing work commonly relies on fine-tuning weights transferred from ImageNet pretraining, which is suboptimal due to drastically different image characteristics, or rule-based label extraction from the textual report data paired with medical images, which is inaccurate and hard to generalize. Meanwhile, several recent studies show exciting results from unsupervised contrastive learning from natural images, but we find these methods help little on medical images because of their high inter-class similarity. We propose ConVIRT, an alternative unsupervised strategy to learn medical visual representations by exploiting naturally occurring paired descriptive text. Our new method of pretraining medical image encoders with the paired text data via a bidirectional contrastive objective between the two modalities is domain-agnostic, and requires no additional expert input. We test ConVIRT by transferring our pretrained weights to 4 medical image classification tasks and 2 zero-shot retrieval tasks, and show that it leads to image representations that considerably outperform strong baselines in most settings. Notably, in all 4 classification tasks, our method requires only 10% as much labeled training data as an ImageNet initialized counterpart to achieve better or comparable performance, demonstrating superior data efficiency.}
}

@inproceedings{wang-etal-2022-medclip,
    title = "{M}ed{CLIP}: Contrastive Learning from Unpaired Medical Images and Text",
    author = "Wang, Zifeng  and
      Wu, Zhenbang  and
      Agarwal, Dinesh  and
      Sun, Jimeng",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.256/",
    doi = "10.18653/v1/2022.emnlp-main.256",
    pages = "3876--3887",
    abstract = "Existing vision-text contrastive learning like CLIP aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction. However, medical image-text datasets are orders of magnitude below the general images and captions from the internet. Moreover, previous methods encounter many false negatives, i.e., images and reports from separate patients probably carry the same semantics but are wrongly treated as negatives. In this paper, we decouple images and texts for multimodal contrastive learning, thus scaling the usable training data in a combinatorial magnitude with low cost. We also propose to replace the InfoNCE loss with semantic matching loss based on medical knowledge to eliminate false negatives in contrastive learning. We prove that MedCLIP is a simple yet effective framework: it outperforms state-of-the-art methods on zero-shot prediction, supervised classification, and image-text retrieval. Surprisingly, we observe that with only 20K pre-training data, MedCLIP wins over the state-of-the-art method (using 200K data). The code is available at https://github.com/RyanWangZf/MedCLIP."
}

@INPROCEEDINGS{9710099,
  author={Huang, Shih-Cheng and Shen, Liyue and Lungren, Matthew P. and Yeung, Serena},
  booktitle={2021 IEEE/CVF International Conference on Computer Vision (ICCV)}, 
  title={GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-efficient Medical Image Recognition}, 
  year={2021},
  volume={},
  number={},
  pages={3922-3931},
  keywords={Representation learning;Deep learning;Training;Image segmentation;Image recognition;Image analysis;Neural networks;Medical;biological;and cell microscopy;Representation learning;Transfer/Low-shot/Semi/Unsupervised Learning;Vision + language},
  doi={10.1109/ICCV48922.2021.00391}}

@misc{hamamci2025developinggeneralistfoundationmodels,
      title={Developing Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography}, 
      author={Ibrahim Ethem Hamamci and Sezgin Er and Chenyu Wang and Furkan Almas and Ayse Gulnihan Simsek and Sevval Nil Esirgun and Irem Dogan and Omer Faruk Durugol and Benjamin Hou and Suprosanna Shit and Weicheng Dai and Murong Xu and Hadrien Reynaud and Muhammed Furkan Dasdelen and Bastian Wittmann and Tamaz Amiranashvili and Enis Simsar and Mehmet Simsar and Emine Bensu Erdemir and Abdullah Alanbay and Anjany Sekuboyina and Berkan Lafci and Ahmet Kaplan and Zhiyong Lu and Malgorzata Polacin and Bernhard Kainz and Christian Bluethgen and Kayhan Batmanghelich and Mehmet Kemal Ozdemir and Bjoern Menze},
      year={2025},
      eprint={2403.17834},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2403.17834}, 
}

@misc{zhao2025scalablelanguageimagepretraining3d,
      title={Towards Scalable Language-Image Pre-training for 3D Medical Imaging}, 
      author={Chenhui Zhao and Yiwei Lyu and Asadur Chowdury and Edward Harake and Akhil Kondepudi and Akshay Rao and Xinhai Hou and Honglak Lee and Todd Hollon},
      year={2025},
      eprint={2505.21862},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.21862}, 
}

@misc{shui2025largescalefinegrainedvisionlanguagepretraining,
      title={Large-scale and Fine-grained Vision-language Pre-training for Enhanced CT Image Understanding}, 
      author={Zhongyi Shui and Jianpeng Zhang and Weiwei Cao and Sinuo Wang and Ruizhe Guo and Le Lu and Lin Yang and Xianghua Ye and Tingbo Liang and Qi Zhang and Ling Zhang},
      year={2025},
      eprint={2501.14548},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2501.14548}, 
}

@misc{carreira2018quovadisactionrecognition,
      title={Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset}, 
      author={Joao Carreira and Andrew Zisserman},
      year={2018},
      eprint={1705.07750},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1705.07750}, 
}

@misc{li2022clinicallongformerclinicalbigbirdtransformerslong,
      title={Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences}, 
      author={Yikuan Li and Ramsey M. Wehbe and Faraz S. Ahmad and Hanyin Wang and Yuan Luo},
      year={2022},
      eprint={2201.11838},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2201.11838}, 
}

@misc{dosovitskiy2021imageworth16x16words,
      title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, 
      author={Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
      year={2021},
      eprint={2010.11929},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2010.11929}, 
}

@misc{jacobgilpytorchcam,
  title={PyTorch library for CAM methods},
  author={Jacob Gildenblat and contributors},
  year={2021},
  publisher={GitHub},
  howpublished={\url{https://github.com/jacobgil/pytorch-grad-cam}},
}

@ARTICLE{Wei2023-po,
  title    = "Deep Learning Methods in Medical {Image-Based} Hepatocellular
              Carcinoma Diagnosis: A Systematic Review and {Meta-Analysis}",
  author   = "Wei, Qiuxia and Tan, Nengren and Xiong, Shiyu and Luo, Wanrong
              and Xia, Haiying and Luo, Baoming",
  abstract = "(1) Background: The aim of our research was to systematically
              review papers specifically focused on the hepatocellular
              carcinoma (HCC) diagnostic performance of DL methods based on
              medical images. (2) Materials: To identify related studies, a
              comprehensive search was conducted in prominent databases,
              including Embase, IEEE, PubMed, Web of Science, and the Cochrane
              Library. The search was limited to studies published before 3
              July 2023. The inclusion criteria consisted of studies that
              either developed or utilized DL methods to diagnose HCC using
              medical images. To extract data, binary information on diagnostic
              accuracy was collected to determine the outcomes of interest,
              namely, the sensitivity, specificity, and area under the curve
              (AUC). (3) Results: Among the forty-eight initially identified
              eligible studies, thirty studies were included in the
              meta-analysis. The pooled sensitivity was 89\% (95\% CI: 87-91),
              the specificity was 90\% (95\% CI: 87-92), and the AUC was 0.95
              (95\% CI: 0.93-0.97). Analyses of subgroups based on medical
              image methods (contrast-enhanced and non-contrast-enhanced
              images), imaging modalities (ultrasound, magnetic resonance
              imaging, and computed tomography), and comparisons between DL
              methods and clinicians consistently showed the acceptable
              diagnostic performance of DL models. The publication bias and
              high heterogeneity observed between studies and subgroups can
              potentially result in an overestimation of the diagnostic
              accuracy of DL methods in medical imaging. (4) Conclusions: To
              improve future studies, it would be advantageous to establish
              more rigorous reporting standards that specifically address the
              challenges associated with DL research in this particular field.",
  journal  = "Cancers (Basel)",
  volume   =  15,
  number   =  23,
  month    =  dec,
  year     =  2023,
  address  = "Switzerland",
  keywords = "deep learning methods; diagnosis; hepatocellular carcinoma;
              medical Image",
  language = "en"
}

@misc{zhang2025biomedclipmultimodalbiomedicalfoundation,
      title={BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs}, 
      author={Sheng Zhang and Yanbo Xu and Naoto Usuyama and Hanwen Xu and Jaspreet Bagga and Robert Tinn and Sam Preston and Rajesh Rao and Mu Wei and Naveen Valluri and Cliff Wong and Andrea Tupini and Yu Wang and Matt Mazzola and Swadheen Shukla and Lars Liden and Jianfeng Gao and Angela Crabtree and Brian Piening and Carlo Bifulco and Matthew P. Lungren and Tristan Naumann and Sheng Wang and Hoifung Poon},
      year={2025},
      eprint={2303.00915},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2303.00915}, 
}

@misc{qwen3technicalreport,
      title={Qwen3 Technical Report}, 
      author={Qwen Team},
      year={2025},
      eprint={2505.09388},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.09388}, 
}

@inproceedings{
Zhang*2020BERTScore:,
title={BERTScore: Evaluating Text Generation with BERT},
author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=SkeHuCVFDr}
}

@inproceedings{Ostmeier_2024,
   title={GREEN: Generative Radiology Report Evaluation and Error Notation},
   url={http://dx.doi.org/10.18653/v1/2024.findings-emnlp.21},
   DOI={10.18653/v1/2024.findings-emnlp.21},
   booktitle={Findings of the Association for Computational Linguistics: EMNLP 2024},
   publisher={Association for Computational Linguistics},
   author={Ostmeier, Sophie and Xu, Justin and Chen, Zhihong and Varma, Maya and Blankemeier, Louis and Bluethgen, Christian and Md, Arne Edward Michalson and Moseley, Michael and Langlotz, Curtis and Chaudhari, Akshay S and Delbrouck, Jean-Benoit},
   year={2024},
   pages={374–390} }

@misc{wang2021understandingbehaviourcontrastiveloss,
      title={Understanding the Behaviour of Contrastive Loss}, 
      author={Feng Wang and Huaping Liu},
      year={2021},
      eprint={2012.09740},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2012.09740}, 
}

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding}, 
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748}, 
}

@inproceedings{
kartika2025selfsupervised,
title={Self-Supervised Adaptation of 3D Abdominal Vision-Language Model for {HCC} Classification: A Comparative Study of Full Fine-Tuning and {PEFT}},
author={Febryan Putra Kartika and Cheng-Yu Ma and Ying-Jia Lin and Chi-Tung Cheng and Kuan-Fu Chen},
booktitle={Submitted to Medical Imaging with Deep Learning},
year={2025},
url={https://openreview.net/forum?id=9zEZlQKUD3},
note={under review}
}

@article{doi:10.1148/ryai.2020190138,
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.},
title = {A Vertebral Segmentation Dataset with Fracture                     Grading},
journal = {Radiology: Artificial Intelligence},
volume = {2},
number = {4},
pages = {e190138},
year = {2020},
doi = {10.1148/ryai.2020190138},
    note ={PMID: 33937831},

URL = { 
    
        https://doi.org/10.1148/ryai.2020190138
    
    

},
eprint = { 
    
        https://doi.org/10.1148/ryai.2020190138
    
    

}
,
    abstract = { Keywords: CT, Computer Aided Diagnosis (CAD), Computer Applications-General (Informatics), Convolutional Neural Network (CNN), Diagnosis, Neural Networks, Perceptron, Segmentation, Skeletal-Axial, Spine, Supervised learning © The Author(s) 2020. Published by the Radiological Society of North America under a CC BY 4.0 license. Supplemental material is available for this article. }
}
