@InProceedings{r1,
author = {Li, Yawei and Tsiminaki, Vagia and Timofte, Radu and Pollefeys, Marc and Gool, Luc Van},
title = {3D Appearance Super-Resolution With Deep Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
@article{r2,
  title={Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography},
  author={Nagayama, Yasunori and Emoto, Takafumi and Kato, Yuki and Kidoh, Masafumi and Oda, Seitaro and Sakabe, Daisuke and Funama, Yoshinori and Nakaura, Takeshi and Hayashi, Hidetaka and Takada, Sentaro and others},
  journal={European Radiology},
  volume={33},
  number={12},
  pages={8488--8500},
  year={2023},
  publisher={Springer}
}

@article{r3,
title = {CNN-based superresolution reconstruction of 3D MR images using thick-slice scans},
journal = {Biocybernetics and Biomedical Engineering},
volume = {40},
number = {1},
pages = {111-125},
year = {2020},
issn = {0208-5216},
doi = {https://doi.org/10.1016/j.bbe.2019.10.003},
url = {https://www.sciencedirect.com/science/article/pii/S0208521619304711},
author = {Jakub Jurek and Marek Kociński and Andrzej Materka and Marcin Elgalal and Agata Majos}
}

@ARTICLE{r4,
  author={Nan, Yang and Ser, Javier Del and Tang, Zeyu and Tang, Peng and Xing, Xiaodan and Fang, Yingying and Herrera, Francisco and Pedrycz, Witold and Walsh, Simon and Yang, Guang},
  journal={IEEE Transactions on Neural Networks and Learning Systems}, 
  title={Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation}, 
  year={2024},
  volume={35},
  number={6},
  pages={7391-7404},
  keywords={Lung;Measurement;Robustness;Fuzzy logic;Lung cancer;Neural networks;Image segmentation;Airway segmentation;COVID-19;fuzzy attention;fuzzy neural networks;pulmonary fibrosis},
  doi={10.1109/TNNLS.2023.3269223}}

@article{r5,
author = {Das, Rangan and Sen, Sagnik and Maulik, Ujjwal},
title = {A Survey on Fuzzy Deep Neural Networks},
year = {2020},
issue_date = {May 2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {53},
number = {3},
issn = {0360-0300},
url = {https://doi.org/10.1145/3369798},
doi = {10.1145/3369798},
abstract = {Deep neural networks are a class of powerful machine learning model that uses successive layers of non-linear processing units to extract features from data. However, the training process of such networks is quite computationally intensive and uses commonly used optimization methods that do not guarantee optimum performance. Furthermore, deep learning methods are often sensitive to noise in data and do not operate well in areas where data are incomplete. An alternative, yet little explored, method in enhancing deep learning performance is the use of fuzzy systems. Fuzzy systems have been previously used in conjunction with neural networks. This survey explores the different ways in which deep learning is improved with fuzzy logic systems. The techniques are classified based on how the two paradigms are combined. Finally, the real-life applications of the models are also explored.},
journal = {ACM Comput. Surv.},
month = {may},
articleno = {54},
numpages = {25},
keywords = {Deep architecture, ensemble models, fuzzy systems, integrated models, parallel models, sequential models}
}
@ARTICLE{r6,
  author={Georgescu, Mariana-Iuliana and Ionescu, Radu Tudor and Verga, Nicolae},
  journal={IEEE Access}, 
  title={Convolutional Neural Networks With Intermediate Loss for 3D Super-Resolution of CT and MRI Scans}, 
  year={2020},
  volume={8},
  number={},
  pages={49112-49124},
  doi={10.1109/ACCESS.2020.2980266}}

@mastersthesis{r7,
  title={Impact of Gan-Based Lesion-Focused Medical Image Super-Resolution on Radiomic Feature Robustness},
  author={de Farias, Erick Costa},
  year={2023},
  school={Universidade NOVA de Lisboa (Portugal)}
}

@article{r8,
  title={Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography},
  author={Nagayama, Yasunori and Emoto, Takafumi and Kato, Yuki and Kidoh, Masafumi and Oda, Seitaro and Sakabe, Daisuke and Funama, Yoshinori and Nakaura, Takeshi and Hayashi, Hidetaka and Takada, Sentaro and others},
  journal={European Radiology},
  volume={33},
  number={12},
  pages={8488--8500},
  year={2023},
  publisher={Springer}
}

@article{r9,
title = {CNN-based superresolution reconstruction of 3D MR images using thick-slice scans},
journal = {Biocybernetics and Biomedical Engineering},
volume = {40},
number = {1},
pages = {111-125},
year = {2020},
issn = {0208-5216},
doi = {https://doi.org/10.1016/j.bbe.2019.10.003},
url = {https://www.sciencedirect.com/science/article/pii/S0208521619304711},
author = {Jakub Jurek and Marek Kociński and Andrzej Materka and Marcin Elgalal and Agata Majos}
}

@article{r10,
  title={Highly accurate facial nerve segmentation refinement from CBCT/CT imaging using a super-resolution classification approach},
  author={Lu, Ping and Barazzetti, Livia and Chandran, Vimal and Gavaghan, Kate and Weber, Stefan and Gerber, Nicolas and Reyes, Mauricio},
  journal={IEEE transactions on biomedical engineering},
  volume={65},
  number={1},
  pages={178--188},
  year={2017},
  publisher={IEEE}
}

@article{r11,
  title={3D deep learning on medical images: a review},
  author={Singh, Satya P and Wang, Lipo and Gupta, Sukrit and Goli, Haveesh and Padmanabhan, Parasuraman and Guly{\'a}s, Bal{\'a}zs},
  journal={Sensors},
  volume={20},
  number={18},
  pages={5097},
  year={2020},
  publisher={MDPI}
}

@article{r12,
  title={A survey on fuzzy deep neural networks},
  author={Das, Rangan and Sen, Sagnik and Maulik, Ujjwal},
  journal={ACM Computing Surveys (CSUR)},
  volume={53},
  number={3},
  pages={1--25},
  year={2020},
  publisher={ACM New York, NY, USA}
}

@article{r13,
  title={Semantic segmentation of breast ultrasound image with fuzzy deep learning network and breast anatomy constraints},
  author={Huang, Kuan and Zhang, Yingtao and Cheng, Heng-Da and Xing, Ping and Zhang, Boyu},
  journal={Neurocomputing},
  volume={450},
  pages={319--335},
  year={2021},
  publisher={Elsevier}
}

@article{r14,
  title={Multimodal infant brain segmentation by fuzzy-informed deep learning},
  author={Ding, Weiping and Abdel-Basset, Mohamed and Hawash, Hossam and Pedrycz, Witold},
  journal={IEEE Transactions on Fuzzy Systems},
  volume={30},
  number={4},
  pages={1088--1101},
  year={2021},
  publisher={IEEE}
}

@article{r15,
  title={Membership function formulation methods for fuzzy logic systems: A comprehensive review},
  author={Jain, Arpit and Sharma, Abhinav},
  journal={Journal of Critical Reviews},
  volume={7},
  number={19},
  pages={8717--8733},
  year={2020}
}
@inproceedings{r16,
  title={Is Autoencoder Truly Applicable for 3d CT Super-Resolution?},
  author={Luo, Weixun and Xing, Xiaodan and Yang, Guang},
  booktitle={2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)},
  pages={1--4},
  year={2023},
  organization={IEEE}
}
@misc{r17,
  author = {Unknown},
  url = {https://www.materialise.com/en/healthcare/},
  year = {June 4, 2024},
  title = {Mimics Innovation Suite | Medical Image Analysis Software}
}
@article{r18,
  title={Attention u-net: Learning where to look for the pancreas. arXiv 2018},
  author={Oktay, Ozan and Schlemper, Jo and Folgoc, Loic Le and Lee, Matthew and Heinrich, Mattias and Misawa, Kazunari and Mori, Kensaku and McDonagh, Steven and Hammerla, Nils Y and Kainz, Bernhard and others},
  journal={arXiv preprint arXiv:1804.03999},
  year={1804}
}

@article{r19,
  title={The fusion of deep learning and fuzzy systems: A state-of-the-art survey},
  author={Zheng, Yuanhang and Xu, Zeshui and Wang, Xinxin},
  journal={IEEE Transactions on Fuzzy Systems},
  volume={30},
  number={8},
  pages={2783--2799},
  year={2021},
  publisher={IEEE}
}

@article{r20,
  title={A novel approach to generate type-1 fuzzy triangular and trapezoidal membership functions to improve the classification accuracy},
  author={Azam, Muhammad Hamza and Hasan, Mohd Hilmi and Hassan, Saima and Abdulkadir, Said Jadid},
  journal={Symmetry},
  volume={13},
  number={10},
  pages={1932},
  year={2021},
  publisher={MDPI}
}
@article{r21,
  title={Fuzzy model-based sparse clustering with multivariate t-mixtures},
  author={Ali, Wajid and Yang, Miin-Shen and Ali, Mehboob and Ud-Din, Saif},
  journal={Applied Artificial Intelligence},
  volume={37},
  number={1},
  pages={2169299},
  year={2023},
  publisher={Taylor \& Francis}
}
@article{r22,
  title={Fuzzy sets and fuzzy logic: theory and applications},
  author={Klir, George J and Yuan, Bo},
  journal={Possibility Theory versus Probab. Theory},
  volume={32},
  number={2},
  pages={207--208},
  year={1996}
}

@article{r23,
  title={Fusion of medical images based on salient features extraction by PSO optimized fuzzy logic in NSST domain},
  author={Gao, Yuan and Ma, Shiwei and Liu, Jingjing and Liu, Yanyan and Zhang, Xianxia},
  journal={Biomedical Signal Processing and Control},
  volume={69},
  pages={102852},
  year={2021},
  publisher={Elsevier}
}
@inproceedings{r24,
  title={Ccnet: Criss-cross attention for semantic segmentation},
  author={Huang, Zilong and Wang, Xinggang and Huang, Lichao and Huang, Chang and Wei, Yunchao and Liu, Wenyu},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={603--612},
  year={2019}
}

@inproceedings{r25,
  title={Cat: Cross attention in vision transformer},
  author={Lin, Hezheng and Cheng, Xing and Wu, Xiangyu and Shen, Dong},
  booktitle={2022 IEEE international conference on multimedia and expo (ICME)},
  pages={1--6},
  year={2022},
  organization={IEEE}
}

@article{r26,
  title={Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma},
  author={Pan, Liangrui and Wang, Hetian and Wang, Lian and Ji, Boya and Liu, Mingting and Chongcheawchamnan, Mitchai and Yuan, Jin and Peng, Shaoliang},
  journal={Biomedical Signal Processing and Control},
  volume={77},
  pages={103824},
  year={2022},
  publisher={Elsevier}
}

@article{r27,
  title={Extraction of airways from CT (EXACT'09)},
  author={Lo, Pechin and Van Ginneken, Bram and Reinhardt, Joseph M and Yavarna, Tarunashree and De Jong, Pim A and Irving, Benjamin and Fetita, Catalin and Ortner, Margarete and Pinho, R{\^o}mulo and Sijbers, Jan and others},
  journal={IEEE Transactions on Medical Imaging},
  volume={31},
  number={11},
  pages={2093--2107},
  year={2012},
  publisher={IEEE}
}

@article{r28,
  title={The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans},
  author={Armato III, Samuel G and McLennan, Geoffrey and Bidaut, Luc and McNitt-Gray, Michael F and Meyer, Charles R and Reeves, Anthony P and Zhao, Binsheng and Aberle, Denise R and Henschke, Claudia I and Hoffman, Eric A and others},
  journal={Medical physics},
  volume={38},
  number={2},
  pages={915--931},
  year={2011},
  publisher={Wiley Online Library}
}
@article{r29,
title = {Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge},
journal = {Medical Image Analysis},
volume = {97},
pages = {103253},
year = {2024},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2024.103253},
url = {https://www.sciencedirect.com/science/article/pii/S1361841524001786},
author = {Yang Nan and Xiaodan Xing and Shiyi Wang and Zeyu Tang and Federico N Felder and Sheng Zhang and Roberta Eufrasia Ledda and Xiaoliu Ding and Ruiqi Yu and Weiping Liu and Feng Shi and Tianyang Sun and Zehong Cao and Minghui Zhang and Yun Gu and Hanxiao Zhang and Jian Gao and Pingyu Wang and Wen Tang and Pengxin Yu and Han Kang and Junqiang Chen and Xing Lu and Boyu Zhang and Michail Mamalakis and Francesco Prinzi and Gianluca Carlini and Lisa Cuneo and Abhirup Banerjee and Zhaohu Xing and Lei Zhu and Zacharia Mesbah and Dhruv Jain and Tsiry Mayet and Hongyu Yuan and Qing Lyu and Abdul Qayyum and Moona Mazher and Athol Wells and Simon LF Walsh and Guang Yang}
}
@misc{r30,
  author = {Unknown},
  url = {https://www.slicer.org/},
  year = {June 4, 2024},
  title = {3D Slicer image computing platform | 3D Slicer}
}

@misc{r31,
  author = {Unknown},
  url = {http://www.itksnap.org/pmwiki/pmwiki.php},
  year = {June 4, 2024},
  title = {ITK-SNAP Home}
}

@inproceedings{r32,
  title={Texture Defragmentation for Photo-Reconstructed 3D Models},
  author={Maggiordomo, Andrea and Cignoni, Paolo and Tarini, Marco},
  booktitle={Computer Graphics Forum},
  volume={40},
  pages={65--78},
  year={2021},
  organization={Wiley Online Library}
}
@inproceedings{r33,
  title={Brain tumor segmentation and survival prediction using 3D attention UNet},
  author={Islam, Mobarakol and Vibashan, VS and Jose, V Jeya Maria and Wijethilake, Navodini and Utkarsh, Uppal and Ren, Hongliang},
  booktitle={Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part I 5},
  pages={262--272},
  year={2020},
  organization={Springer}
}

@InProceedings{r34,
author="Dong, Chao
and Loy, Chen Change
and He, Kaiming
and Tang, Xiaoou",
editor="Fleet, David
and Pajdla, Tomas
and Schiele, Bernt
and Tuytelaars, Tinne",
title="Learning a Deep Convolutional Network for Image Super-Resolution",
booktitle="Computer Vision -- ECCV 2014",
year="2014",
publisher="Springer International Publishing",
address="Cham",
pages="184--199",
}
@inproceedings{r35,
  title={Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network},
  author={Shi, Wenzhe and Caballero, Jose and Husz{\'a}r, Ferenc and Totz, Johannes and Aitken, Andrew P and Bishop, Rob and Rueckert, Daniel and Wang, Zehan},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1874--1883},
  year={2016}
}
@inproceedings{r36,
  title={Accurate image super-resolution using very deep convolutional networks},
  author={Kim, Jiwon and Lee, Jung Kwon and Lee, Kyoung Mu},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1646--1654},
  year={2016}
}
@inproceedings{r37,
  title={Lightweight image super-resolution with information multi-distillation network},
  author={Hui, Zheng and Gao, Xinbo and Yang, Yunchu and Wang, Xiumei},
  booktitle={Proceedings of the 27th acm international conference on multimedia},
  pages={2024--2032},
  year={2019}
}
@inproceedings{r38,
  title={Efficient image super-resolution using pixel attention},
  author={Zhao, Hengyuan and Kong, Xiangtao and He, Jingwen and Qiao, Yu and Dong, Chao},
  booktitle={Computer Vision--ECCV 2020 Workshops: Glasgow, UK, August 23--28, 2020, Proceedings, Part III 16},
  pages={56--72},
  year={2020},
  organization={Springer}
}
@article{r39,
  title={Double paths network with residual information distillation for improving lung CT image super resolution},
  author={Chen, Yihan and Zheng, Qianying and Chen, Jiansen},
  journal={Biomedical Signal Processing and Control},
  volume={73},
  pages={103412},
  year={2022},
  publisher={Elsevier}
}
@article{r40,
  title={Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images},
  author={Wu, Yanan and Qi, Shouliang and Wang, Meihuan and Zhao, Shuiqing and Pang, Haowen and Xu, Jiaxuan and Bai, Long and Ren, Hongliang},
  journal={Medical \& Biological Engineering \& Computing},
  volume={61},
  number={10},
  pages={2649--2663},
  year={2023},
  publisher={Springer}
}

@misc{r41,
      title={SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation}, 
      author={Shehan Perera and Pouyan Navard and Alper Yilmaz},
      year={2024},
      eprint={2404.10156},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2404.10156}, 
}

@article{r42,
  title={Swin Unet3D: a three-dimensional medical image segmentation network combining vision transformer and convolution},
  author={Cai, Yimin and Long, Yuqing and Han, Zhenggong and Liu, Mingkun and Zheng, Yuchen and Yang, Wei and Chen, Liming},
  journal={BMC medical informatics and decision making},
  volume={23},
  number={1},
  pages={33},
  year={2023},
  publisher={Springer}
}

@inproceedings{r43,
  title={Quantification of credal uncertainty in machine learning: A critical analysis and empirical comparison},
  author={H{\"u}llermeier, Eyke and Destercke, S{\'e}bastien and Shaker, Mohammad Hossein},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={548--557},
  year={2022},
  organization={PMLR}
}


@inproceedings{r44,
  title={Is the volume of a credal set a good measure for epistemic uncertainty?},
  author={Sale, Yusuf and Caprio, Michele and H{\"o}llermeier, Eyke},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={1795--1804},
  year={2023},
  organization={PMLR}
}

@article{r45,
  title={Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation},
  author={Amyar, Amine and Modzelewski, Romain and Li, Hua and Ruan, Su},
  journal={Computers in biology and medicine},
  volume={126},
  pages={104037},
  year={2020},
  publisher={Elsevier}
}

@article{r46,
  title={Automatic 3D liver location and segmentation via convolutional neural network and graph cut},
  author={Lu, Fang and Wu, Fa and Hu, Peijun and Peng, Zhiyi and Kong, Dexing},
  journal={International journal of computer assisted radiology and surgery},
  volume={12},
  pages={171--182},
  year={2017},
  publisher={Springer}
}

@article{r47,
  title={Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation},
  author={Amyar, Amine and Modzelewski, Romain and Li, Hua and Ruan, Su},
  journal={Computers in biology and medicine},
  volume={126},
  pages={104037},
  year={2020},
  publisher={Elsevier}
}
