@Article{cancers13071590,
AUTHOR = {Alzubaidi, Laith and Al-Amidie, Muthana and Al-Asadi, Ahmed and Humaidi, Amjad J. and Al-Shamma, Omran and Fadhel, Mohammed A. and Zhang, Jinglan and Santamaría, J. and Duan, Ye},
TITLE = {Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data},
JOURNAL = {Cancers},
VOLUME = {13},
YEAR = {2021},
NUMBER = {7},
ARTICLE-NUMBER = {1590},
URL = {https://www.mdpi.com/2072-6694/13/7/1590},
PubMedID = {33808207},
ISSN = {2072-6694},
DOI = {10.3390/cancers13071590}
}
@article{lew2024publicly,
  title={A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI},
  author={Lew, Christopher O and Harouni, Majid and Kirksey, Ella R and Kang, Elianne J and Dong, Haoyu and Gu, Hanxue and Grimm, Lars J and Walsh, Ruth and Lowell, Dorothy A and Mazurowski, Maciej A},
  journal={Scientific reports},
  volume={14},
  number={1},
  pages={5383},
  year={2024},
  publisher={Nature Publishing Group UK London}
}
@inproceedings{cciccek20163d, title={3D U-Net: learning dense volumetric segmentation from sparse annotation}, author={{\c{C}}i{\c{c}}ek, {\"O}zg{\"u}n and Abdulkadir, Ahmed and Lienkamp, Soeren S and Brox, Thomas and Ronneberger, Olaf}, booktitle={Medical Image Computing and Computer-Assisted Intervention--MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19}, pages={424--432}, year={2016}, organization={Springer} } 

@InProceedings{yeung_sli2vol,
author="Yeung, Pak-Hei
and Namburete, Ana I. L.
and Xie, Weidi",
editor="de Bruijne, Marleen
and Cattin, Philippe C.
and Cotin, St{\'e}phane
and Padoy, Nicolas
and Speidel, Stefanie
and Zheng, Yefeng
and Essert, Caroline",
title="Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-supervised Learning",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2021",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="69--79",
isbn="978-3-030-87196-3"
}
@InProceedings{wu_ssa,
author="Wu, Yixuan
and Zheng, Bo
and Chen, Jintai
and Chen, Danny Z.
and Wu, Jian",
editor="Wang, Linwei
and Dou, Qi
and Fletcher, P. Thomas
and Speidel, Stefanie
and Li, Shuo",
title="Self-learning and One-Shot Learning Based Single-Slice Annotation for 3D Medical Image Segmentation",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022",
year="2022",
publisher="Springer Nature Switzerland",
address="Cham",
pages="244--254",
isbn="978-3-031-16452-1"
}

@inproceedings{zheng2019biomedical, title={Biomedical image segmentation via representative annotation}, author={Zheng, Hao and Yang, Lin and Chen, Jianxu and Han, Jun and Zhang, Yizhe and Liang, Peixian and Zhao, Zhuo and Wang, Chaoli and Chen, Danny Z}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, volume={33}, pages={5901--5908}, year={2019}}

@inproceedings{zheng2020annotation,
  title={An annotation sparsification strategy for 3D medical image segmentation via representative selection and self-training},
  author={Zheng, Hao and Zhang, Yizhe and Yang, Lin and Wang, Chaoli and Chen, Danny Z},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={04},
  pages={6925--6932},
  year={2020}
}

@article{zhang2019sparse,
  title={A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation},
  author={Zhang, Zhenxi and Li, Jie and Zhong, Zhusi and Jiao, Zhicheng and Gao, Xinbo},
  journal={arXiv preprint arXiv:1906.07367},
  year={2019}
}
@article{bilic2023liver,
  title={The liver tumor segmentation benchmark (lits)},
  author={Bilic, Patrick and Christ, Patrick and Li, Hongwei Bran and Vorontsov, Eugene and Ben-Cohen, Avi and Kaissis, Georgios and Szeskin, Adi and Jacobs, Colin and Mamani, Gabriel Efrain Humpire and Chartrand, Gabriel and others},
  journal={Medical Image Analysis},
  volume={84},
  pages={102680},
  year={2023},
  publisher={Elsevier}
}
@article{Saha2018AML,
  title={A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features},
  author={Ashirbani Saha and Michael R. Harowicz and Lars J. Grimm and Connie E. Kim and Sujata V. Ghate and Ruth Walsh and Maciej A. Mazurowski},
  journal={British Journal of Cancer},
  year={2018},
  volume={119},
  pages={508 - 516},
  url={https://api.semanticscholar.org/CorpusID:49902015}
}
@article{liew2022large,
  title={A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms},
  author={Liew, Sook-Lei and Lo, Bethany P and Donnelly, Miranda R and Zavaliangos-Petropulu, Artemis and Jeong, Jessica N and Barisano, Giuseppe and Hutton, Alexandre and Simon, Julia P and Juliano, Julia M and Suri, Anisha and others},
  journal={Scientific data},
  volume={9},
  number={1},
  pages={320},
  year={2022},
  publisher={Nature Publishing Group UK London}
}
@article{punn2020inception,
  title={Inception u-net architecture for semantic segmentation to identify nuclei in microscopy cell images},
  author={Punn, Narinder Singh and Agarwal, Sonali},
  journal={ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)},
  volume={16},
  number={1},
  pages={1--15},
  year={2020},
  publisher={ACM New York, NY, USA}
}

@article{xie2021segformer,
  title={SegFormer: Simple and efficient design for semantic segmentation with transformers},
  author={Xie, Enze and Wang, Wenhai and Yu, Zhiding and Anandkumar, Anima and Alvarez, Jose M and Luo, Ping},
  journal={Advances in neural information processing systems},
  volume={34},
  pages={12077--12090},
  year={2021}
}
@article{camwnet,
author = {Liu, Zhenhong and Yuan, Hongfang and Wang, Huaqing},
title = {CAM-Wnet: An effective solution for accurate pulmonary embolism segmentation},
journal = {Medical Physics},
volume = {49},
number = {8},
pages = {5294-5303},
keywords = {attention mechanism, image segmentation, pulmonary embolism, pyramid pooling module, U-Net},
doi = {https://doi.org/10.1002/mp.15719},
url = {https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.15719},
eprint = {https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.15719},
year = {2022}
}

@article{perez-garcia_torchio_2021,
   title = {{TorchIO}: a {Python} library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning},
   journal = {Computer Methods and Programs in Biomedicine},
   pages = {106236},
   year = {2021},
   issn = {0169-2607},
   doi = {https://doi.org/10.1016/j.cmpb.2021.106236},
   url = {https://www.sciencedirect.com/science/article/pii/S0169260721003102},
   author = {P{\'e}rez-Garc{\'i}a, Fernando and Sparks, Rachel and Ourselin, S{\'e}bastien},
}
@inproceedings{myronenko20193d,
  title={3D MRI brain tumor segmentation using autoencoder regularization},
  author={Myronenko, Andriy},
  booktitle={Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4},
  pages={311--320},
  year={2019},
  organization={Springer}
}
@inproceedings{hatamizadeh2022unetr,
  title={Unetr: Transformers for 3d medical image segmentation},
  author={Hatamizadeh, Ali and Tang, Yucheng and Nath, Vishwesh and Yang, Dong and Myronenko, Andriy and Landman, Bennett and Roth, Holger R and Xu, Daguang},
  booktitle={Proceedings of the IEEE/CVF winter conference on applications of computer vision},
  pages={574--584},
  year={2022}
}
@inproceedings{
oktay2018attention,
title={Attention U-Net: Learning Where to Look for the Pancreas},
author={Ozan Oktay and Jo Schlemper and Loic Le Folgoc and Matthew Lee and Mattias Heinrich and Kazunari Misawa and Kensaku Mori and Steven McDonagh and Nils Y Hammerla and Bernhard Kainz and Ben Glocker and Daniel Rueckert},
booktitle={Medical Imaging with Deep Learning},
year={2018},
url={https://openreview.net/forum?id=Skft7cijM}
}
@article{ren2021survey,
  title={A survey of deep active learning},
  author={Ren, Pengzhen and Xiao, Yun and Chang, Xiaojun and Huang, Po-Yao and Li, Zhihui and Gupta, Brij B and Chen, Xiaojiang and Wang, Xin},
  journal={ACM computing surveys (CSUR)},
  volume={54},
  number={9},
  pages={1--40},
  year={2021},
  publisher={ACM New York, NY}
}
@inproceedings{
sener2018active,
title={Active Learning for Convolutional Neural Networks: A Core-Set Approach},
author={Ozan Sener and Silvio Savarese},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=H1aIuk-RW},
}
@inproceedings{gal2017deep,
  title={Deep bayesian active learning with image data},
  author={Gal, Yarin and Islam, Riashat and Ghahramani, Zoubin},
  booktitle={International conference on machine learning},
  pages={1183--1192},
  year={2017},
  organization={PMLR}
}
@inproceedings{vorontsov2021label,
  title={Label noise in segmentation networks: mitigation must deal with bias},
  author={Vorontsov, Eugene and Kadoury, Samuel},
  booktitle={Deep Generative Models, and Data Augmentation, Labelling, and Imperfections: First Workshop, DGM4MICCAI 2021, and First Workshop, DALI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings 1},
  pages={251--258},
  year={2021},
  organization={Springer}
}
@inproceedings{zlateski2018importance,
  title={On the importance of label quality for semantic segmentation},
  author={Zlateski, Aleksandar and Jaroensri, Ronnachai and Sharma, Prafull and Durand, Fr{\'e}do},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={1479--1487},
  year={2018}
}
@article{zhang2023efficiently,
  title={How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model.},
  author={Zhang, Yixin and Zhao, Shen and Gu, Hanxue and Mazurowski, Maciej A},
  journal={CoRR},
  year={2023}
}