Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L: Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data 2021, 8:1–74. https://doi.org/10.1186/s40537-021-00444-8.

Article  Google Scholar 

LeCun Y, Bengio Y, Hinton G: Deep learning. Nature 2015, 521(7553):436–444.  https://doi.org/10.1038/nature14539.

Article  CAS  PubMed  Google Scholar 

Bullock J, Cuesta-Lázaro C, Quera-Bofarull A: XNet: a convolutional neural network (CNN) implementation for medical x-ray image segmentation suitable for small datasets. Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging 2019, 10953:453–463. https://doi.org/10.48550/arXiv.1812.00548.

Dozen A, Komatsu M, Sakai A, Komatsu R, Shozu K, Machino H, Yasutomi S, Arakaki T, Asada K, Kaneko S et al: Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information. Biomolecules 2020, 10(11):1526. https://doi.org/10.3390/biom10111526.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Farooq A, Anwar S, Awais M, Rehman S: A deep CNN based multi-class classification of Alzheimer’s disease using MRI. 2017 IEEE International Conference on Imaging systems and techniques (IST) 2017:1–6. https://doi.org/10.1109/IST.2017.8261460.

Jinnai S, Yamazaki N, Hirano Y, Sugawara Y, Ohe Y, Hamamoto R: The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning. Biomolecules 2020, 10(8):1123. https://doi.org/10.3390/biom10081123.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kobayashi K, Hataya R, Kurose Y, Miyake M, Takahashi M, Nakagawa A, Harada T, Hamamoto R: Decomposing Normal and Abnormal Features of Medical Images for Content-Based Image Retrieval of Glioma Imaging. Medical Image Analysis 2021, 74:102227. https://doi.org/10.1016/j.media.2021.102227.

Article  PubMed  Google Scholar 

Komatsu M, Sakai A, Komatsu R, Matsuoka R, Yasutomi S, Shozu K, Dozen A, Machino H, Hidaka H, Arakaki T et al: Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning. Applied Sciences 2021, 11(1):371. https://doi.org/10.3390/app11010371.

Article  CAS  Google Scholar 

Milletari F, Ahmadi S-A, Kroll C, Plate A, Rozanski V, Maiostre J, Levin J, Dietrich O, Ertl-Wagner B, Bötzel K: Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound. Computer Vision and Image Understanding 2017, 164:92–102. https://doi.org/10.48550/arXiv.1601.07014.

Article  Google Scholar 

Yamada M, Saito Y, Imaoka H, Saiko M, Yamada S, Kondo H, Takamaru H, Sakamoto T, Sese J, Kuchiba A et al: Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Sci Rep 2019, 9(1):14465. https://doi.org/10.1038/s41598-019-50567-5.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yadav D, Rathor S: Bone fracture detection and classification using deep learning approach. 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC) 2020:282–285. https://doi.org/10.1109/PARC49193.2020.236611.

Rahman T, Chowdhury ME, Khandakar A, Islam KR, Islam KF, Mahbub ZB, Kadir MA, Kashem S: Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray. Applied Sciences 2020, 10(9):3233. https://doi.org/10.3390/app10093233.

Article  CAS  Google Scholar 

Hamamoto R, Suvarna K, Yamada M, Kobayashi K, Shinkai N, Miyake M, Takahashi M, Jinnai S, Shimoyama R, Sakai A et al: Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine. Cancers (Basel) 2020, 12(12):3532. https://doi.org/10.3390/cancers12123532.

Article  PubMed  Google Scholar 

Asada K, Kobayashi K, Joutard S, Tubaki M, Takahashi S, Takasawa K, Komatsu M, Kaneko S, Sese J, Hamamoto R: Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer. Biomolecules 2020, 10(4):524. https://doi.org/10.3390/biom10040524.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kobayashi K, Bolatkan A, Shiina S, Hamamoto R: Fully-Connected Neural Networks with Reduced Parameterization for Predicting Histological Types of Lung Cancer from Somatic Mutations. Biomolecules 2020, 10(9):1249. https://doi.org/10.3390/biom10091249.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Takahashi S, Asada K, Takasawa K, Shimoyama R, Sakai A, Bolatkan A, Shinkai N, Kobayashi K, Komatsu M, Kaneko S et al: Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data. Biomolecules 2020, 10(10):1460. https://doi.org/10.3390/biom10101460.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Shin TY, Kim H, Lee J-H, Choi J-S, Min H-S, Cho H, Kim K, Kang G, Kim J, Yoon S: Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver. Investigative and clinical urology 2020, 61(6):555. https://doi.org/10.4111/icu.20200086.

Article  PubMed  PubMed Central  Google Scholar 

Arab A, Chinda B, Medvedev G, Siu W, Guo H, Gu T, Moreno S, Hamarneh G, Ester M, Song X: A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT. Scientific Reports 2020, 10(1):19389. https://doi.org/10.1038/s41598-020-76459-7

Article  CAS  PubMed  PubMed Central  Google Scholar 

Williams DP: On the use of tiny convolutional neural networks for human-expert-level classification performance in sonar imagery. IEEE Journal of Oceanic Engineering 2020, 46(1):236–260. https://doi.org/10.1109/JOE.2019.2963041.

Article  Google Scholar 

Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D: Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE international conference on computer vision 2017:618–626. https://doi.org/10.48550/arXiv.1610.02391.

Takahashi S, Takahashi M, Kinoshita M, Miyake M, Kawaguchi R, Shinojima N, Mukasa A, Saito K, Nagane M, Otani R et al: Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities. Cancers (Basel) 2021, 13(6). https://doi.org/10.3390/cancers13061415.

Nam H, Lee H, Park J, Yoon W, Yoo D: Reducing domain gap by reducing style bias. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2021:8690–8699. https://doi.org/10.48550/arXiv.1910.11645.

Yan W, Wang Y, Gu S, Huang L, Yan F, Xia L, Tao Q: The domain shift problem of medical image segmentation and vendor-adaptation by Unet-GAN. Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II 22 2019:623–631. https://doi.org/10.48550/arXiv.1910.13681.

Agarwal P, Nachappa M, Gautam CK: Multi-Scale Recurrent Neural Networks for Medical Image Classification. 2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) 2024:1–6. https://doi.org/10.1109/ICOCWC60930.2024.10470694.

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I: Attention is all you need. Advances in neural information processing systems 2017, 30. https://doi.org/10.48550/arXiv.1706.03762.

Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 2020. https://doi.org/10.48550/arXiv.2010.11929.

Liu Y, Wu Y-H, Sun G, Zhang L, Chhatkuli A, Van Gool L: Vision transformers with hierarchical attention. arXiv preprint arXiv:210603180 2021. https://doi.org/10.48550/arXiv.2106.03180.

Han K, Wang Y, Chen H, Chen X, Guo J, Liu Z, Tang Y, Xiao A, Xu C, Xu Y: A survey on vision transformer. IEEE transactions on pattern analysis and machine intelligence 2022, 45(1):87–110. https://doi.org/10.1109/TPAMI.2022.3152247.

Article  PubMed  Google Scholar 

Hatamizadeh A, Yin H, Heinrich G, Kautz J, Molchanov P: Global context vision transformers. International Conference on Machine Learning 2023:12633–12646. https://doi.org/10.48550/arXiv.2206.09959.

He K, Gan C, Li Z, Rekik I, Yin Z, Ji W, Gao Y, Wang Q, Zhang J, Shen D: Transformers in medical image analysis. Intelligent Medicine 2023, 3(1):59–78. https://doi.org/10.1016/j.imed.2022.07.002.

Article  Google Scholar 

Barzekar H, Patel Y, Tong L, Yu Z: MultiNet with Transformers: A Model for Cancer Diagnosis Using Images. arXiv preprint arXiv:230109007 2023. https://doi.org/10.48550/arXiv.2301.09007.

Stassin S, Corduant V, Mahmoudi SA, Siebert X: Explainability and Evaluation of Vision Transformers: An In-Depth Experimental Study. Electronics 2023, 13(1):175. https://doi.org/10.3390/electronics13010175.

Article  Google Scholar 

Chetoui M, Akhloufi MA: Explainable vision transformers and radiomics for covid-19 detection in chest x-rays. Journal of Clinical Medicine 2022, 11(11):3013. https://doi.org/10.3390/jcm11113013.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Dipto SM, Reza MT, Rahman MNJ, Parvez MZ, Barua PD, Chakraborty S: An XAI Integrated Identification System of White Blood Cell Type Using Variants of Vision Transformer. International Conference on Interactive Collaborative Robotics 2023:303–315. https://doi.org/10.1007/978-3-031-35308-6_26.

Cao Y-H, Yu H, Wu J: Training vision transformers with only 2040 images. European Conference on Computer Vision 2022:220–237. https://doi.org/10.48550/arXiv.2201.10728.

Lee SH, Lee S, Song BC: Vision transformer for small-size datasets. arXiv preprint arXiv:211213492 2021. https://doi.org/10.48550/arXiv.2112.13492.

Liu Y, Sangineto E, Bi W, Sebe N, Lepri B, Nadai M: Efficient training of visual transformers with small datasets. Advances in Neural Information Processing Systems 2021, 34:23818–23830. https://doi.org/10.48550/arXiv.2106.03746.

Article  Google Scholar 

Habib G, Saleem TJ, Lall B: Knowledge distillation in vision transformers: A critical review. arXiv preprint arXiv:230202108 2023. https://doi.org/10.48550/arXiv.2302.02108.

Youn E, Prabhu S, Chen S: Compressing Vision Transformers for Low-Resource Visual Learning. arXiv preprint arXiv:230902617 2023. https://doi.org/10.48550/arXiv.2309.02617.

Wang X, Zhang LL, Wang Y, Yang M: Towards efficient vision transformer inference: A first study of transformers on mobile devices. Proceedings of the 23rd Annual International Workshop on Mobile Computing Systems and Applications 2022:1–7. https://doi.org/10.1145/3508396.3512869.

Fukushima K: Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 1980, 36(4):193–202. https://doi.org/10.1007/BF00344251.

Article  CAS  PubMed  Google Scholar 

LeCun Y, Bottou L, Bengio Y, Haffner P: Gradient-based learning applied to document recognition. Proceedings of the IEEE 1998, 86(11):2278–2324. https://doi.org/10.1109/5.726791.

Hamamoto R, Komatsu M, Takasawa K, Asada K, Kaneko S: Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine. Biomolecules 2020, 10(1):62. https://doi.org/10.3390/biom10010062.

Krizhevsky A, Sutskever I, Hinton GE: ImageNet classification with deep convolutional neural networks. Communications of the ACM 2017, 60(6):84–90. https://doi.org/10.1145/3065386.