@String(PAMI = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV = {Int. J. Comput. Vis.})
@String(CVPR= {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV= {Int. Conf. Comput. Vis.})
@String(ECCV= {Eur. Conf. Comput. Vis.})
@String(NIPS= {Adv. Neural Inform. Process. Syst.})
@String(ICPR = {Int. Conf. Pattern Recog.})
@String(BMVC= {Brit. Mach. Vis. Conf.})
@String(TOG= {ACM Trans. Graph.})
@String(TIP  = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TMM  = {IEEE Trans. Multimedia})
@String(ACMMM= {ACM Int. Conf. Multimedia})
@String(ICME = {Int. Conf. Multimedia and Expo})
@String(ICASSP=	{ICASSP})
@String(ICIP = {IEEE Int. Conf. Image Process.})
@String(ACCV  = {ACCV})
@String(ICLR = {Int. Conf. Learn. Represent.})
@String(IJCAI = {IJCAI})
@String(PR   = {Pattern Recognition})
@String(AAAI = {AAAI})
@String(CVPRW= {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@String(SPL	= {IEEE Sign. Process. Letters})
@String(VR   = {Vis. Res.})
@String(JOV	 = {J. Vis.})
@String(TVC  = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF  = {Comput. Graph. Forum})
@String(CVM = {Computational Visual Media})


@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NIPS  = {NeurIPS})
@String(ICPR  = {ICPR})
@String(BMVC  =	{BMVC})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(ICME  =	{ICME})
@String(ICASSP=	{ICASSP})
@String(ICIP  = {ICIP})
@String(ACCV  = {ACCV})
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@String(IJCAI = {IJCAI})
@String(PR = {PR})
@String(AAAI = {AAAI})
@String(CVPRW= {CVPRW})
@String(CSVT = {IEEE TCSVT})



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@misc{Authors14b,
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 year = 2014
}

@article{Alpher02,
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journal = PAMI,
volume = 12,
number = 1,
pages = {234--778},
year = 2002
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title = {Frobnication revisited},
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volume = 13,
number = 1,
pages = {234--778},
year = 2003
}

@article{Alpher04,
author = {FirstName Alpher and FirstName Fotheringham-Smythe and FirstName Gamow},
title = {Can a machine frobnicate?},
journal = {Journal of Foo},
volume = 14,
number = 1,
pages = {234--778},
year = 2004
}

@inproceedings{Alpher05,
author = {FirstName Alpher and FirstName Gamow},
title = {Can a computer frobnicate?},
booktitle = CVPR,
pages = {234--778},
year = 2005
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@inproceedings{refinenet,
author="He, Xinzi
and Yu, Zhen
and Wang, Tianfu
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title="Skin Lesion Segmentation via Deep RefineNet",
booktitle="Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support ",
year="2017",
address="Cham",
pages="303--311",
isbn="978-3-319-67558-9"
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@ARTICLE{fca-net,
  author={Singh, Vivek Kumar and Abdel-Nasser, Mohamed and Rashwan, Hatem A. and Akram, Farhan and Pandey, Nidhi and Lalande, Alain and Presles, Benoit and Romani, Santiago and Puig, Domenec},
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  keywords={Lesions;Skin;Image segmentation;Convolution;Image color analysis;Feature extraction;Kernel;Skin lesion;conditional generative adversarial network;channel attention;factorized kernel;residual convolution},
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@INPROCEEDINGS{focusnet,
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  booktitle={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}, 
  title={Focusnet: An Attention-Based Fully Convolutional Network for Medical Image Segmentation}, 
  year={2019},
  volume={},
  number={},
  pages={455-458},
  keywords={Image segmentation;Biomedical imaging;Lung;Computer architecture;Training;Melanoma;Semantic segmentation;attention in CNNs;medical imaging;U-Net;residual connections},
  doi={10.1109/ISBI.2019.8759477}
}

@article{BI201978,
title = {Step-wise integration of deep class-specific learning for dermoscopic image segmentation},
journal = {Pattern Recognition},
volume = {85},
pages = {78-89},
year = {2019},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2018.08.001},
url = {https://www.sciencedirect.com/science/article/pii/S0031320318302772},
author = {Lei Bi and Jinman Kim and Euijoon Ahn and Ashnil Kumar and Dagan Feng and Michael Fulham},
keywords = {Dermoscopic, Melanoma, Segmentation, Fully convolutional networks (FCN)},
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@ARTICLE{8990108,
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  journal={IEEE Transactions on Medical Imaging}, 
  title={A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification}, 
  year={2020},
  volume={39},
  number={7},
  pages={2482-2493},
  keywords={Lesions;Image segmentation;Skin;Task analysis;Feature extraction;Decoding;Computational modeling;Skin lesion segmentation;skin lesion classification;deep convolutional neural network;dermoscopy},
  doi={10.1109/TMI.2020.2972964}
}

@article{LEI2020101716,
title = {Skin lesion segmentation via generative adversarial networks with dual discriminators},
journal = {Medical Image Analysis},
volume = {64},
pages = {101716},
year = {2020},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2020.101716},
url = {https://www.sciencedirect.com/science/article/pii/S1361841520300803},
author = {Baiying Lei and Zaimin Xia and Feng Jiang and Xudong Jiang and Zongyuan Ge and Yanwu Xu and Jing Qin and Siping Chen and Tianfu Wang and Shuqiang Wang},
keywords = {Skin lesion segmentation, Generative adversarial network, Dense convolution U-Net, Dual discriminators}
}

@article{Jayapriya,
author = {Jayapriya, Kalyanakumar and Jacob, I. Jeena},
year = {2019},
month = {11},
pages = {},
title = {Hybrid fully convolutional networks‐based skin lesion segmentation and melanoma detection using deep feature},
volume = {30},
journal = {International Journal of Imaging Systems and Technology},
doi = {10.1002/ima.22377}
}

@article{PEZHMANPOUR2020113129,
title = {Transform domain representation-driven convolutional neural networks for skin lesion segmentation},
journal = {Expert Systems with Applications},
volume = {144},
pages = {113129},
year = {2020},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2019.113129},
url = {https://www.sciencedirect.com/science/article/pii/S0957417419308462},
author = {Mansoureh {Pezhman Pour} and Huseyin Seker},
keywords = {Convolutional neural network, Dermoscopic features, Melanoma, Skin lesion segmentation, Transform domain}
}

@article{10.1117/1.JMI.6.2.024001,
author = {Lei Zhang and Guang Yang and Xujiong Ye},
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volume = {6},
journal = {Journal of Medical Imaging},
number = {2},
publisher = {SPIE},
pages = {024001},
keywords = {melanoma, skin lesion segmentation, fully convolutional networks, textons, Skin, Image segmentation, Data modeling, Network architectures, Image filtering, Melanoma, Performance modeling, Convolution, Surgery, Associative arrays},
year = {2019},
doi = {10.1117/1.JMI.6.2.024001},
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@InProceedings{10.1007/978-3-030-87193-2_2,
author="Zhang, Yundong
and Liu, Huiye
and Hu, Qiang",
title="TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2021",
year="2021",
address="Cham",
pages="14--24",
isbn="978-3-030-87193-2"
}

@InProceedings{10.1007/978-3-030-87193-2_31,
author="Ji, Yuanfeng
and Zhang, Ruimao
and Wang, Huijie
and Li, Zhen
and Wu, Lingyun
and Zhang, Shaoting
and Luo, Ping",
title="Multi-compound Transformer for Accurate Biomedical Image Segmentation",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2021",
year="2021",
address="Cham",
pages="326--336",
isbn="978-3-030-87193-2"
}

@article{WU2022102327,
title = {FAT-Net: Feature adaptive transformers for automated skin lesion segmentation},
journal = {Medical Image Analysis},
volume = {76},
pages = {102327},
year = {2022},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2021.102327},
url = {https://www.sciencedirect.com/science/article/pii/S1361841521003728},
author = {Huisi Wu and Shihuai Chen and Guilian Chen and Wei Wang and Baiying Lei and Zhenkun Wen},
keywords = {Feature adaptive transformer, Convolutional neural networks, Skin lesion segmentation, Memory-efficient decoder},
}

@ARTICLE{9104928,
  author={Li, Xiaomeng and Yu, Lequan and Chen, Hao and Fu, Chi-Wing and Xing, Lei and Heng, Pheng-Ann},
  journal={IEEE Transactions on Neural Networks and Learning Systems}, 
  title={Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation}, 
  year={2021},
  volume={32},
  number={2},
  pages={523-534},
  keywords={Image segmentation;Biomedical imaging;Skin;Liver;Task analysis;Lesions;Computed tomography;Liver segmentation;optic disk (OD) segmentation;self-ensembling;semisupervised learning;skin lesion segmentation},
  doi={10.1109/TNNLS.2020.2995319}
}

@INPROCEEDINGS{9098527,
  author={Li, Yuexiang and Chen, Jiawei and Zheng, Yefeng},
  booktitle={2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)}, 
  title={A Multi-Task Self-Supervised Learning Framework for Scopy Images}, 
  year={2020},
  volume={},
  number={},
  pages={2005-2009},
  keywords={Task analysis;Image color analysis;Skin;Image segmentation;Lesions;Biomedical imaging;Machine learning;Self-supervised learning;Multi-task learning;Colorization},
  doi={10.1109/ISBI45749.2020.9098527}
}

@article{ZHANG2022369,
title = {Dynamic prototypical feature representation learning framework for semi-supervised skin lesion segmentation},
journal = {Neurocomputing},
volume = {507},
pages = {369-382},
year = {2022},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2022.08.039},
url = {https://www.sciencedirect.com/science/article/pii/S092523122201013X},
author = {Zhenxi Zhang and Chunna Tian and Xinbo Gao and Cui Wang and Xue Feng and Harrison X. Bai and Zhicheng Jiao},
keywords = {Semi-supervised learning, Skin lesion segmentation, Contrastive learning, Memory relational learning},
}

@ARTICLE{9957055,
  author={Alahmadi, Mohammad D. and Alghamdi, Wajdi},
  journal={IEEE Access}, 
  title={Semi-Supervised Skin Lesion Segmentation With Coupling CNN and Transformer Features}, 
  year={2022},
  volume={10},
  number={},
  pages={122560-122569},
  keywords={Image segmentation;Lesions;Skin;Transformers;Training;Task analysis;Medical diagnostic imaging;Skin lesion;CNN;transformer;semi-supervised;segmentation},
  doi={10.1109/ACCESS.2022.3224005}
}

@INPROCEEDINGS{9871734,
  author={Zhao, Ziyuan and Lu, Wenjing and Zeng, Zeng and Xu, Kaixin and Veeravalli, Bharadwaj and Guan, Cuntai},
  booktitle={2022 44th Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)}, 
  title={Self-supervised Assisted Active Learning for Skin Lesion Segmentation}, 
  year={2022},
  volume={},
  number={},
  pages={5043-5046},
  keywords={Image segmentation;Costs;Uncertainty;Annotations;Self-supervised learning;Feature extraction;Skin},
  doi={10.1109/EMBC48229.2022.9871734}
}

@INPROCEEDINGS{9761620,
  author={Wang, Zhonghua and Lyu, Junyan and Luo, Wenhao and Tang, Xiaoying},
  booktitle={2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)}, 
  title={Superpixel Inpainting For Self-Supervised Skin Lesion Segmentation from Dermoscopic Images}, 
  year={2022},
  volume={},
  number={},
  pages={1-4},
  keywords={Image segmentation;Annotations;Skin;Robustness;Lesions;Noise measurement;Task analysis;Skin Lesion;SLIC Superpixel;Image In-painting;Self-supervised Learning;Image Segmentation},
  doi={10.1109/ISBI52829.2022.9761620}
}

@inproceedings{zou2021pseudoseg,
    title={PseudoSeg: Designing Pseudo Labels for Semantic Segmentation},
    author={Yuliang Zou and Zizhao Zhang and Han Zhang and Chun-Liang Li and Xiao Bian and Jia-Bin Huang and Tomas Pfister},
    booktitle={International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=-TwO99rbVRu}
}

@INPROCEEDINGS{9157032,
  author={Ouali, Yassine and Hudelot, Céline and Tami, Myriam},
  booktitle={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Semi-Supervised Semantic Segmentation With Cross-Consistency Training}, 
  year={2020},
  volume={},
  number={},
  pages={12671-12681},
  keywords={Decoding;Training;Perturbation methods;Semantics;Image segmentation;Predictive models;Task analysis},
  doi={10.1109/CVPR42600.2020.01269}
}

@INPROCEEDINGS{10203865,
  author={Yang, Lihe and Qi, Lei and Feng, Litong and Zhang, Wayne and Shi, Yinghuan},
  booktitle={2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation}, 
  year={2023},
  volume={},
  number={},
  pages={7236-7246},
  keywords={Protocols;Perturbation methods;Semantic segmentation;Pipelines;Manuals;Streaming media;Pattern recognition;Segmentation;grouping and shape analysis},
  doi={10.1109/CVPR52729.2023.00699}
}

@INPROCEEDINGS{9577639,
  author={Chen, Xiaokang and Yuan, Yuhui and Zeng, Gang and Wang, Jingdong},
  booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision}, 
  year={2021},
  volume={},
  number={},
  pages={2613-2622},
  keywords={Training;Image segmentation;Computer vision;Semantics;Training data;Pattern recognition;Standards},
  doi={10.1109/CVPR46437.2021.00264}
}

@INPROCEEDINGS{9880095,
  author={Fan, Jiashuo and Gao, Bin and Jin, Huan and Jiang, Lihui},
  booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={UCC: Uncertainty guided Cross-head Cotraining for Semi-Supervised Semantic Segmentation}, 
  year={2022},
  volume={},
  number={},
  pages={9937-9946},
  keywords={Training;Deep learning;Computer vision;Uncertainty;Protocols;Semantics;Neural networks;Self-\& semi-\& meta- Machine learning},
  doi={10.1109/CVPR52688.2022.00971}
}

@INPROCEEDINGS{9008296,
  author={Yun, Sangdoo and Han, Dongyoon and Chun, Sanghyuk and Oh, Seong Joon and Yoo, Youngjoon and Choe, Junsuk},
  booktitle={2019 IEEE/CVF International Conference on Computer Vision (ICCV)}, 
  title={CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features}, 
  year={2019},
  volume={},
  number={},
  pages={6022-6031},
  keywords={Training;Task analysis;Object detection;Computer vision;Computational modeling;Dogs;Robustness},
  doi={10.1109/ICCV.2019.00612}
}

@inproceedings{laine2017temporal,
title={Temporal Ensembling for Semi-Supervised Learning},
author={Samuli Laine and Timo Aila},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=BJ6oOfqge}
}

@InProceedings{10.1007/978-3-031-25066-8_9,
author="Cao, Hu
and Wang, Yueyue
and Chen, Joy
and Jiang, Dongsheng
and Zhang, Xiaopeng
and Tian, Qi
and Wang, Manning",
editor="Karlinsky, Leonid
and Michaeli, Tomer
and Nishino, Ko",
title="Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation",
booktitle="Computer Vision -- ECCV 2022 Workshops",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="205--218",
isbn="978-3-031-25066-8"
}

@INPROCEEDINGS{8363547,
  author={Codella, Noel C. F. and Gutman, David and Celebi, M. Emre and Helba, Brian and Marchetti, Michael A. and Dusza, Stephen W. and Kalloo, Aadi and Liopyris, Konstantinos and Mishra, Nabin and Kittler, Harald and Halpern, Allan},
  booktitle={2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)}, 
  title={Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)}, 
  year={2018},
  volume={},
  number={},
  pages={168-172},
  doi={10.1109/ISBI.2018.8363547}
}

@article{tschandl2018ham10000,
  title={The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions},
  author={Tschandl, Philipp and Rosendahl, Cliff and Kittler, Harald},
  journal={Scientific data},
  volume={5},
  number={1},
  pages={1--9},
  year={2018},
  publisher={Nature Publishing Group}
}

@article{Tschandl2020HumancomputerCF,
  title={Human–computer collaboration for skin cancer recognition},
  author={Philipp Tschandl and Christoph Rinner and Zoe Apalla and Giuseppe Argenziano and Noel C. F. Codella and Allan C. Halpern and Monika Janda and Aimilios Lallas and Caterina Longo and Josep Malvehy and Josep Malvehy and John Paoli and John Paoli and Susana Puig and Susana Puig and Cliff Rosendahl and Hans Peter Soyer and Iris Zalaudek and Harald Kittler},
  journal={Nature Medicine},
  year={2020},
  volume={26},
  pages={1229 - 1234},
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}

@INPROCEEDINGS{9878761,
  author={Kwon, Donghyeon and Kwak, Suha},
  booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Semi-supervised Semantic Segmentation with Error Localization Network}, 
  year={2022},
  volume={},
  number={},
  pages={9947-9957},
  keywords={Training;Location awareness;Degradation;Heart;Image resolution;Semisupervised learning;Pattern recognition;Self-& semi-& meta- Segmentation;grouping and shape analysis},
  doi={10.1109/CVPR52688.2022.00972}}

@InProceedings{10.1007/978-3-030-58526-6_9,
author="Mendel, Robert
and de Souza, Luis Antonio
and Rauber, David
and Papa, Jo{\~a}o Paulo
and Palm, Christoph",
editor="Vedaldi, Andrea
and Bischof, Horst
and Brox, Thomas
and Frahm, Jan-Michael",
title="Semi-supervised Segmentation Based on Error-Correcting Supervision",
booktitle="Computer Vision -- ECCV 2020",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="141--157",
isbn="978-3-030-58526-6"
}

@INPROCEEDINGS{9880151,
  author={Yang, Lihe and Zhuo, Wei and Qi, Lei and Shi, Yinghuan and Gao, Yang},
  booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={ST++: Make Self-trainingWork Better for Semi-supervised Semantic Segmentation}, 
  year={2022},
  volume={},
  number={},
  pages={4258-4267},
  keywords={Training;Image segmentation;Semantics;Pipelines;Predictive models;Stability analysis;Pattern recognition;Segmentation;grouping and shape analysis; Self-& semi-& meta- & unsupervised learning},
  doi={10.1109/CVPR52688.2022.00423}
}

@INPROCEEDINGS{9879201,
  author={Liu, Yuyuan and Tian, Yu and Chen, Yuanhong and Liu, Fengbei and Belagiannis, Vasileios and Carneiro, Gustavo},
  booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation}, 
  year={2022},
  volume={},
  number={},
  pages={4248-4257},
  keywords={Training;Image segmentation;Shape;Perturbation methods;Semantics;Mean square error methods;Predictive models;Segmentation;grouping and shape analysis; Self-& semi-& meta- & unsupervised learning},
  doi={10.1109/CVPR52688.2022.00422}}

@INPROCEEDINGS{9423297,
  author={Olsson, Viktor and Tranheden, Wilhelm and Pinto, Juliano and Svensson, Lennart},
  booktitle={2021 IEEE Winter Conference on Applications of Computer Vision (WACV)}, 
  title={ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning}, 
  year={2021},
  volume={},
  number={},
  pages={1368-1377},
  keywords={Training;Image segmentation;Computer vision;Conferences;Semantics;Manuals;Semisupervised learning},
  doi={10.1109/WACV48630.2021.00141}}

@inproceedings{10.5555/3495724.3495775,
author = {Sohn, Kihyuk and Berthelot, David and Li, Chun-Liang and Zhang, Zizhao and Carlini, Nicholas and Cubuk, Ekin D. and Kurakin, Alex and Zhang, Han and Raffel, Colin},
title = {FixMatch: simplifying semi-supervised learning with consistency and confidence},
year = {2020},
isbn = {9781713829546},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
booktitle = {Proceedings of the 34th International Conference on Neural Information Processing Systems},
articleno = {51},
numpages = {13},
location = {Vancouver, BC, Canada},
series = {NIPS '20}
}

@InProceedings{10.1007/978-3-030-01267-0_9,
author="Qiao, Siyuan
and Shen, Wei
and Zhang, Zhishuai
and Wang, Bo
and Yuille, Alan",
editor="Ferrari, Vittorio
and Hebert, Martial
and Sminchisescu, Cristian
and Weiss, Yair",
title="Deep Co-Training for Semi-Supervised Image Recognition",
booktitle="Computer Vision -- ECCV 2018",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="142--159",
isbn="978-3-030-01267-0"
}

@article{wu2024image,
  title={Image-Feature Weak-to-Strong Consistency: An Enhanced Paradigm for Semi-Supervised Learning},
  author={Wu, Zhiyu and Cui, Jinshi},
  journal={arXiv preprint arXiv:2408.12614},
  year={2024}
}

@article{ngo2024dual,
  title={Dual dynamic consistency regularization for semi-supervised domain adaptation},
  author={Ngo, Ba Hung and Lam, Ba Thinh and Nguyen, Thanh Huy and Dinh, Quang Vinh and Choi, Tae Jong},
  journal={IEEE Access},
  year={2024},
  publisher={IEEE}
}

@inproceedings{ccvc,
  title={Conflict-based cross-view consistency for semi-supervised semantic segmentation},
  author={Wang, Zicheng and Zhao, Zhen and Xing, Xiaoxia and Xu, Dong and Kong, Xiangyu and Zhou, Luping},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={19585--19595},
  year={2023}
}

@article{marchetti20233d,
  title={3D Whole-body skin imaging for automated melanoma detection},
  author={Marchetti, MA and Nazir, ZH and Nanda, JK and Dusza, SW and D'Alessandro, BM and DeFazio, J and Halpern, AC and Rotemberg, VM and Marghoob, AA},
  journal={Journal of the European Academy of Dermatology and Venereology},
  volume={37},
  number={5},
  pages={945--950},
  year={2023},
  publisher={Wiley Online Library}
}

@ARTICLE{lefed,
  author={Zeng, Qingjie and Xie, Yutong and Lu, Zilin and Lu, Mengkang and Zhang, Jingfeng and Xia, Yong},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Consistency-Guided Differential Decoding for Enhancing Semi-Supervised Medical Image Segmentation}, 
  year={2025},
  volume={44},
  number={1},
  pages={44-56},
  doi={10.1109/TMI.2024.3429340}}

@article{zeng2024consistency,
  title={Consistency-guided differential decoding for enhancing semi-supervised medical image segmentation},
  author={Zeng, Qingjie and Xie, Yutong and Lu, Zilin and Lu, Mengkang and Zhang, Jingfeng and Zhou, Yuyin and Xia, Yong},
  journal={IEEE Transactions on Medical Imaging},
  year={2024},
  publisher={IEEE}
}