@book{Lamport:Book:1989,
 author = {Lamport, Leslie},
 title = {Latex: A Document Preparation System},
 year = {1986},
 isbn = {0-201-15790-X},
 publisher = {Addison-Wesley Longman Publishing Co., Inc.},
 address = {Boston, MA, USA},
}

@misc{wolleb2021diffusion,
      title={Diffusion Models for Implicit Image Segmentation Ensembles}, 
      author={Julia Wolleb and Robin Sandkühler and Florentin Bieder and Philippe Valmaggia and Philippe C. Cattin},
      year={2021},
      eprint={2112.03145},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{nichol2021improved,
      title={Improved Denoising Diffusion Probabilistic Models}, 
      author={Alex Nichol and Prafulla Dhariwal},
      year={2021},
      eprint={2102.09672},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{ho2020denoising,
      title={Denoising Diffusion Probabilistic Models}, 
      author={Jonathan Ho and Ajay Jain and Pieter Abbeel},
      year={2020},
      eprint={2006.11239},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{Hinton:arXiv:2015:Distilling,
 title={Distilling the knowledge in a neural network},
 author={Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
 OPTeprinttype={arXiv},
 OPTeprint={1503.02531},
 howpublished={Eprint \href{http://arxiv.org/abs/1503.02531}{arXiv:1503.02531}},
 OPTurl={http://arxiv.org/abs/1503.02531},
 year=2015
}

@article{MedSAM,
  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={arXiv preprint arXiv:2304.12306},
  year={2023}
}

@article{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 Doll{\'a}r, Piotr and Girshick, Ross},
  journal={arXiv:2304.02643},
  year={2023}
}

@InProceedings{selfprompt,
author="Wu, Qi
and Zhang, Yuyao
and Elbatel, Marawan",
editor="Koch, Lisa
and Cardoso, M. Jorge
and Ferrante, Enzo
and Kamnitsas, Konstantinos
and Islam, Mobarakol
and Jiang, Meirui
and Rieke, Nicola
and Tsaftaris, Sotirios A.
and Yang, Dong",
title="Self-prompting Large Vision Models for Few-Shot Medical Image Segmentation",
booktitle="Domain Adaptation and Representation Transfer",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="156--167",
abstract="Recent advancements in large foundation models have shown promising potential in the medical industry due to their flexible prompting capability. One such model, the Segment Anything Model (SAM), a prompt-driven segmentation model, has shown remarkable performance improvements, surpassing state-of-the-art approaches in medical image segmentation. However, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available. In this paper, we propose a novel perspective on self-prompting in medical vision applications. Specifically, we harness the embedding space of SAM to prompt itself through a simple yet effective linear pixel-wise classifier. By preserving the encoding capabilities of the large model, the contextual information from its decoder, and leveraging its interactive promptability, we achieve competitive results on multiple datasets (i.e. improvement of more than 15{\%} compared to fine-tuning the mask decoder using a few images). Our code is available at https://github.com/PeterYYZhang/few-shot-self-prompt-SAM",
isbn="978-3-031-45857-6"
}


@InProceedings{heterogeneous,
author="Shin, Dongik
and Kim, M.D., Beomsuk
and Baek, Seungjun",
editor="Celebi, M. Emre
and Salekin, Md Sirajus
and Kim, Hyunwoo
and Albarqouni, Shadi
and Barata, Catarina
and Halpern, Allan
and Tschandl, Philipp
and Combalia, Marc
and Liu, Yuan
and Zamzmi, Ghada
and Levy, Joshua
and Rangwala, Huzefa
and Reinke, Annika
and Wynn, Diya
and Landman, Bennett
and Jeong, Won-Ki
and Shen, Yiqing
and Deng, Zhongying
and Bakas, Spyridon
and Li, Xiaoxiao
and Qin, Chen
and Rieke, Nicola
and Roth, Holger
and Xu, Daguang",
title="CEmb-SAM: Segment Anything Model with Condition Embedding for Joint Learning from Heterogeneous Datasets",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023 Workshops ",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="275--284",
abstract="Automated segmentation of ultrasound images can assist medical experts with diagnostic and therapeutic procedures. Although using the common modality of ultrasound, one typically needs separate datasets in order to segment, for example, different anatomical structures or lesions with different levels of malignancy. In this paper, we consider the problem of jointly learning from heterogeneous datasets so that the model can improve generalization abilities by leveraging the inherent variability among datasets. We merge the heterogeneous datasets into one dataset and refer to each component dataset as a subgroup. We propose to train a single segmentation model so that the model can adapt to each sub-group. For robust segmentation, we leverage recently proposed Segment Anything model (SAM) in order to incorporate sub-group information into the model. We propose SAM with Condition Embedding block (CEmb-SAM) which encodes sub-group conditions and combines them with image embeddings from SAM. The conditional embedding block effectively adapts SAM to each image sub-group by incorporating dataset properties through learnable parameters for normalization. Experiments show that CEmb-SAM outperforms the baseline methods on ultrasound image segmentation for peripheral nerves and breast cancer. The experiments highlight the effectiveness of CEmb-SAM in learning from heterogeneous datasets in medical image segmentation tasks. The code is publicly available at https://github.com/DongDong500/CEmb-SAM",
isbn="978-3-031-47401-9"
}


@inproceedings{braintumor,
  title={Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation},
  author={Mohammad Peivandi and Jason Bing Zhang and Michael Lu and Dongxiao Zhu and Zhifeng Kou},
  year={2023},
  url={https://api.semanticscholar.org/CorpusID:263829153}
}
@article{eyesegmentation,
  title={Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)},
  author={Virmarie Maquiling and Sean Anthony Byrne and Diederick Christian Niehorster and Marcus Nystr{\"o}m and Enkelejda Kasneci},
  journal={ArXiv},
  year={2023},
  volume={abs/2311.08077},
  url={https://api.semanticscholar.org/CorpusID:265158106}
}

@misc{samus,
      title={SAMUS: Adapting Segment Anything Model for Clinically-Friendly and Generalizable Ultrasound Image Segmentation}, 
      author={Xian Lin and Yangyang Xiang and Li Zhang and Xin Yang and Zengqiang Yan and Li Yu},
      year={2023},
      eprint={2309.06824},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@ARTICLE{CHASE,
  author={Fraz, Muhammad Moazam and Remagnino, Paolo and Hoppe, Andreas and Uyyanonvara, Bunyarit and Rudnicka, Alicja R. and Owen, Christopher G. and Barman, Sarah A.},
  journal={IEEE Transactions on Biomedical Engineering}, 
  title={An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation}, 
  year={2012},
  volume={59},
  number={9},
  pages={2538-2548},
  keywords={Retina;Biomedical imaging;Training;Blood vessels;Databases;Lesions;Vectors;Ensemble classification;medical image analysis;retinal blood vessels;segmentation},
  doi={10.1109/TBME.2012.2205687}}

@article{nestedunet,
  title={UNet++: A Nested U-Net Architecture for Medical Image Segmentation},
  author={Zongwei Zhou and Md Mahfuzur Rahman Siddiquee and Nima Tajbakhsh and Jianming Liang},
  journal={Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, S...},
  year={2018},
  volume={11045},
  pages={
          3-11
        },
  url={https://api.semanticscholar.org/CorpusID:50786304}
}

@article{busi,
title = {Dataset of breast ultrasound images},
journal = {Data in Brief},
volume = {28},
pages = {104863},
year = {2020},
issn = {2352-3409},
doi = {https://doi.org/10.1016/j.dib.2019.104863},
url = {https://www.sciencedirect.com/science/article/pii/S2352340919312181},
author = {Walid Al-Dhabyani and Mohammed Gomaa and Hussien Khaled and Aly Fahmy},
keywords = {Ultrasound, Breast cancer, Medical images, Dataset, Deep learning, Classification, Segmentation, Detection},
abstract = {Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of early deaths. The data presented in this article reviews the medical images of breast cancer using ultrasound scan. Breast Ultrasound Dataset is categorized into three classes: normal, benign, and malignant images. Breast ultrasound images can produce great results in classification, detection, and segmentation of breast cancer when combined with machine learning.}
}

@article{isic,
  title={Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)},
  author={David Gutman and Noel C. F. Codella and Emre M. Celebi and Brian Helba and Michael Marchetti and Nabin K. Mishra and Allan Halpern},
  journal={ArXiv},
  year={2016},
  volume={abs/1605.01397},
  url={https://api.semanticscholar.org/CorpusID:260439738}
}

@article{lungseg,
  title={Curation of the CANDID-PTX Dataset with Free-Text Reports.},
  author={Si-Yang Feng and Damian Azzollini and Ji Soo Kim and Cheng-Kai Jin and Simon Gordon and Jason Yeoh and Eve Kim and Mina Han and Andrew Lee and Aakash Patel and Joy Wu and Martin Urschler and Amy Tsoi Lai Fong and Cameron Simmers and Gregory Patrick Tarr and Stuart Barnard and Ben Wilson},
  journal={Radiology. Artificial intelligence},
  year={2021},
  volume={3 6},
  pages={
          e210136
        },
  url={https://api.semanticscholar.org/CorpusID:243370709}
}

@article{diffusion,
title = {Diffusion models in medical imaging: A comprehensive survey},
journal = {Medical Image Analysis},
volume = {88},
pages = {102846},
year = {2023},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2023.102846},
url = {https://www.sciencedirect.com/science/article/pii/S1361841523001068},
author = {Amirhossein Kazerouni and Ehsan Khodapanah Aghdam and Moein Heidari and Reza Azad and Mohsen Fayyaz and Ilker Hacihaliloglu and Dorit Merhof},
keywords = {Generative models, Diffusion models, Denoising diffusion models, Noise conditioned score networks, Score-based models, Medical imaging, Medical applications, Survey},
abstract = {Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward diffusion stage where the input data is gradually perturbed over several steps by adding Gaussian noise and then learns to reverse the diffusion process to retrieve the desired noise-free data from noisy data samples. Diffusion models are widely appreciated for their strong mode coverage and quality of the generated samples in spite of their known computational burdens. Capitalizing on the advances in computer vision, the field of medical imaging has also observed a growing interest in diffusion models. With the aim of helping the researcher navigate this profusion, this survey intends to provide a comprehensive overview of diffusion models in the discipline of medical imaging. Specifically, we start with an introduction to the solid theoretical foundation and fundamental concepts behind diffusion models and the three generic diffusion modeling frameworks, namely, diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms. To this end, we cover extensive applications of diffusion models in the medical domain, including image-to-image translation, reconstruction, registration, classification, segmentation, denoising, 2/3D generation, anomaly detection, and other medically-related challenges. Furthermore, we emphasize the practical use case of some selected approaches, and then we discuss the limitations of the diffusion models in the medical domain and propose several directions to fulfill the demands of this field. Finally, we gather the overviewed studies with their available open-source implementations at our GitHub.11https://github.com/amirhossein-kz/Awesome-Diffusion-Models-in-Medical-Imaging. We aim to update the relevant latest papers within it regularly.}
}

@InProceedings{10.1007/978-3-319-24574-4_28,
author="Ronneberger, Olaf
and Fischer, Philipp
and Brox, Thomas",
editor="Navab, Nassir
and Hornegger, Joachim
and Wells, William M.
and Frangi, Alejandro F.",
title="U-Net: Convolutional Networks for Biomedical Image Segmentation",
booktitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="234--241",
abstract="There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.",
isbn="978-3-319-24574-4"
}

@INPROCEEDINGS{7785132,
  author={Milletari, Fausto and Navab, Nassir and Ahmadi, Seyed-Ahmad},
  booktitle={2016 Fourth International Conference on 3D Vision (3DV)}, 
  title={V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation}, 
  year={2016},
  volume={},
  number={},
  pages={565-571},
  keywords={Image segmentation;Feature extraction;Biomedical imaging;Three-dimensional displays;Neural networks;Magnetic resonance imaging;Two dimensional displays;Deep learning;segmentation;prostate;machine learning;convolutional neural networks},
  doi={10.1109/3DV.2016.79}}

@inbook{stroke,
author = {Islam, Mobarakol and Natarajan, Rajiv Vaidyanathan and Valanarasu, Jeya Maria Jose},
year = {2019},
month = {01},
pages = {292-300},
title = {Ischemic Stroke Lesion Segmentation Using Adversarial Learning: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I},
isbn = {978-3-030-11722-1},
doi = {10.1007/978-3-030-11723-8_29}
}

@inbook{kiu-net,
author = {Valanarasu, Jeya Maria Jose and Sindagi, Vishwanath and Hacihaliloglu, Ilker and Patel, Vishal},
year = {2020},
month = {09},
pages = {363-373},
title = {KiU-Net: Towards Accurate Segmentation of Biomedical Images Using Over-Complete Representations},
isbn = {978-3-030-59718-4},
doi = {10.1007/978-3-030-59719-1_36}
}
@inproceedings{medtrans,
author = {Valanarasu, Jeya Maria Jose and Oza, Poojan and Hacihaliloglu, Ilker and Patel, Vishal M.},
title = {Medical Transformer: Gated Axial-Attention for Medical Image Segmentation},
year = {2021},
isbn = {978-3-030-87192-5},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-030-87193-2_4},
doi = {10.1007/978-3-030-87193-2_4},
abstract = {Over the past decade, deep convolutional neural networks have been widely adopted for medical image segmentation and shown to achieve adequate performance. However, due to inherent inductive biases present in convolutional architectures, they lack understanding of long-range dependencies in the image. Recently proposed transformer-based architectures that leverage self-attention mechanism encode long-range dependencies and learn representations that are highly expressive. This motivates us to explore transformer-based solutions and study the feasibility of using transformer-based network architectures for medical image segmentation tasks. Majority of existing transformer-based network architectures proposed for vision applications require large-scale datasets to train properly. However, compared to the datasets for vision applications, in medical imaging the number of data samples is relatively low, making it difficult to efficiently train transformers for medical imaging applications. To this end, we propose a gated axial-attention model which extends the existing architectures by introducing an additional control mechanism in the self-attention module. Furthermore, to train the model effectively on medical images, we propose a Local-Global training strategy (LoGo) which further improves the performance. Specifically, we operate on the whole image and patches to learn global and local features, respectively. The proposed Medical Transformer (MedT) is evaluated on three different medical image segmentation datasets and it is shown that it achieves better performance than the convolutional and other related transformer-based architectures. Code:},
booktitle = {Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I},
pages = {36–46},
numpages = {11},
keywords = {Transformers, Medical image segmentation, Self-attention},
location = {Strasbourg, France}
}
@misc{chen2021transunet,
      title={TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation}, 
      author={Jieneng Chen and Yongyi Lu and Qihang Yu and Xiangde Luo and Ehsan Adeli and Yan Wang and Le Lu and Alan L. Yuille and Yuyin Zhou},
      year={2021},
      eprint={2102.04306},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{azad2023foundational,
      title={Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision}, 
      author={Bobby Azad and Reza Azad and Sania Eskandari and Afshin Bozorgpour and Amirhossein Kazerouni and Islem Rekik and Dorit Merhof},
      year={2023},
      eprint={2310.18689},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{zhang2024segment,
      title={Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions}, 
      author={Yichi Zhang and Zhenrong Shen and Rushi Jiao},
      year={2024},
      eprint={2401.03495},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

@misc{lee2024foundation,
      title={Foundation Models for Biomedical Image Segmentation: A Survey}, 
      author={Ho Hin Lee and Yu Gu and Theodore Zhao and Yanbo Xu and Jianwei Yang and Naoto Usuyama and Cliff Wong and Mu Wei and Bennett A. Landman and Yuankai Huo and Alberto Santamaria-Pang and Hoifung Poon},
      year={2024},
      eprint={2401.07654},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@article{fraiwan2023dataset,
  title={A dataset of COVID-19 x-ray chest images},
  author={Fraiwan, Mohammad and Khasawneh, Natheer and Khassawneh, Basheer and Ibnian, Ali},
  journal={Data in Brief},
  volume={47},
  pages={109000},
  year={2023},
  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}
}

@article{ma2024u,
  title={U-mamba: Enhancing long-range dependency for biomedical image segmentation},
  author={Ma, Jun and Li, Feifei and Wang, Bo},
  journal={arXiv preprint arXiv:2401.04722},
  year={2024}
}

@article{goodfellow2020generative,
  title={Generative adversarial networks},
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  journal={Communications of the ACM},
  volume={63},
  number={11},
  pages={139--144},
  year={2020},
  publisher={ACM New York, NY, USA}
}

@misc{lei2023medlsam,
      title={MedLSAM: Localize and Segment Anything Model for 3D CT Images}, 
      author={Wenhui Lei and Xu Wei and Xiaofan Zhang and Kang Li and Shaoting Zhang},
      year={2023},
      eprint={2306.14752},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{yu2023inpaint,
      title={Inpaint Anything: Segment Anything Meets Image Inpainting}, 
      author={Tao Yu and Runseng Feng and Ruoyu Feng and Jinming Liu and Xin Jin and Wenjun Zeng and Zhibo Chen},
      year={2023},
      eprint={2304.06790},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{wang2023caption,
      title={Caption Anything: Interactive Image Description with Diverse Multimodal Controls}, 
      author={Teng Wang and Jinrui Zhang and Junjie Fei and Hao Zheng and Yunlong Tang and Zhe Li and Mingqi Gao and Shanshan Zhao},
      year={2023},
      eprint={2305.02677},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@INPROCEEDINGS {yolov8,
author = {S. Pandey and K. Chen and E. B. Dam},
booktitle = {2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
title = {Comprehensive Multimodal Segmentation in Medical Imaging: Combining YOLOv8 with SAM and HQ-SAM Models},
year = {2023},
volume = {},
issn = {},
pages = {2584-2590},
abstract = {This paper introduces a comprehensive approach for segmenting regions of interest (ROI) in diverse medical imaging datasets, encompassing ultrasound, CT scans, and X-ray images. The proposed method harnesses the capabilities of the YOLOv8 model for approximate boundary box detection across modalities, alongside the Segment Anything Model (SAM) and High Quality (HQ) SAM for fully automatic and precise segmentation. To generate boundary boxes, the YOLOv8 model was trained using a limited set of 100 images and masks from each modality.The results obtained from our approach are extensively computed and analyzed, demonstrating its effectiveness and potential in medical image analysis. Various evaluation metrics, including precision, recall, F1 score, and Dice Score, were employed to quantify the accuracy of the segmentation results. A comparative analysis was conducted to assess the individual and combined performance of the YOLOv8, YOLOv8+SAM, and YOLOv8+HQ-SAM models.The results indicate that the SAM model performs better than the other two models, exhibiting higher segmentation accuracy and overall performance. While HQ-SAM offers potential advantages, its incremental gains over the standard SAM model may not justify the additional computational cost. The YOLOv8+SAM model shows promise for enhancing medical image segmentation and its clinical implications.},
keywords = {image segmentation;analytical models;ultrasonic imaging;computational modeling;computed tomography;lung;computer architecture},
doi = {10.1109/ICCVW60793.2023.00273},
url = {https://doi.ieeecomputersociety.org/10.1109/ICCVW60793.2023.00273},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = {oct}
}

@misc{anand2023oneshot,
      title={One-shot Localization and Segmentation of Medical Images with Foundation Models}, 
      author={Deepa Anand and Gurunath Reddy M and Vanika Singhal and Dattesh D. Shanbhag and Shriram KS and Uday Patil and Chitresh Bhushan and Kavitha Manickam and Dawei Gui and Rakesh Mullick and Avinash Gopal and Parminder Bhatia and Taha Kass-Hout},
      year={2023},
      eprint={2310.18642},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{cui2023allinsam,
      title={All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning}, 
      author={Can Cui and Ruining Deng and Quan Liu and Tianyuan Yao and Shunxing Bao and Lucas W. Remedios and Yucheng Tang and Yuankai Huo},
      year={2023},
      eprint={2307.00290},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{shaharabany2023autosam,
      title={AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder}, 
      author={Tal Shaharabany and Aviad Dahan and Raja Giryes and Lior Wolf},
      year={2023},
      eprint={2306.06370},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@article{Oliveira2023ZeroshotPO,
  title={Zero-shot performance of the Segment Anything Model (SAM) in 2D medical imaging: A comprehensive evaluation and practical guidelines},
  author={Christian Mattjie de Oliveira and Luis Vin{\'i}cius de Moura and Rafaela Cappelari Ravazio and Lucas Silveira Kupssinsk{\"u} and Ot{\'a}vio Parraga and Marcelo Mussi Delucis and Rodrigo C. Barros},
  journal={ArXiv},
  year={2023},
  volume={abs/2305.00109},
  url={https://api.semanticscholar.org/CorpusID:258426532}
}

@InProceedings{Leng_2024_WACV,
    author    = {Leng, Tianang and Zhang, Yiming and Han, Kun and Xie, Xiaohui},
    title     = {Self-Sampling Meta SAM: Enhancing Few-Shot Medical Image Segmentation With Meta-Learning},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2024},
    pages     = {7925-7935}
}

@misc{qi2023selfguided,
      title={Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models}, 
      author={Xiyu Qi and Yifan Wu and Yongqiang Mao and Wenhui Zhang and Yidan Zhang},
      year={2023},
      eprint={2311.13200},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@inproceedings{zhang2023input,
  title={Input augmentation with sam: Boosting medical image segmentation with segmentation foundation model},
  author={Zhang, Yizhe and Zhou, Tao and Wang, Shuo and Liang, Peixian and Zhang, Yejia and Chen, Danny Z},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={129--139},
  year={2023},
  organization={Springer}
}

@misc{chen2023masam,
      title={MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation}, 
      author={Cheng Chen and Juzheng Miao and Dufan Wu and Zhiling Yan and Sekeun Kim and Jiang Hu and Aoxiao Zhong and Zhengliang Liu and Lichao Sun and Xiang Li and Tianming Liu and Pheng-Ann Heng and Quanzheng Li},
      year={2023},
      eprint={2309.08842},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@misc{he2023computervision,
      title={Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets}, 
      author={Sheng He and Rina Bao and Jingpeng Li and Jeffrey Stout and Atle Bjornerud and P. Ellen Grant and Yangming Ou},
      year={2023},
      eprint={2304.09324},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

@misc{deng2023samu,
      title={SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image}, 
      author={Guoyao Deng and Ke Zou and Kai Ren and Meng Wang and Xuedong Yuan and Sancong Ying and Huazhu Fu},
      year={2023},
      eprint={2307.04973},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

 @article{butoi2023universeg,
  title={UniverSeg: Universal Medical Image Segmentation},
  author={Victor Ion Butoi and Jose Javier Gonzalez Ortiz and Tianyu Ma and Mert R. Sabuncu and John Guttag and Adrian V. Dalca},
  journal={International Conference on Computer Vision},
  year={2023}
}