Aiadi O, Khaldi B, Saadeddine C. MDFNet: an unsupervised lightweight network for ear print recognition. J. Ambient. Int. Humanized Comput. 2022;1–14
Aiadi O, Khaldi B. A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases. Biomed Signal Process Control. 2022;78.
Lu L, Zheng Y, Carneiro G, Yang L. Deep learning and convolutional neural networks for medical image computing. Adv Comput Vis Pattern Recognit. 2017;10:978–3.
Wang D, Khosla A, Gargeya R, Irshad H, Beck AH. Deep learning for identifying metastatic breast cancer 2016. arXiv preprint arXiv:1606.05718
Sarraf S, DeSouza DD, Anderson J, Tofighi G, Initiativ ADN. DeepAD: Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI. BioRxiv, 2016;070441
Payan A, Montana G. Predicting Alzheimer’s disease: a neuroimaging study with 3d convolutional neural networks 2015. arXiv preprint arXiv:1502.02506.
Suk H-I, Lee S-W, Shen D, Initiative ADN. Latent feature representation with stacked auto-encoder for ad/mci diagnosis. Brain Struct Funct. 2015;220:841–59.
Kumar S, Payne P, Sotiras A. Normative modeling using multimodal variational autoencoders to identify abnormal brain structural patterns in Alzheimer disease 2021. arXiv preprint arXiv:2110.04903
Ferri R, Babiloni C, Karami V, Triggiani AI, Carducci F, Noce G, Lizio R, Pascarelli MT, Soricelli A, Amenta F, et al. Stacked autoencoders as new models for an accurate Alzheimer’s disease classification support using resting-state EEG and MRI measurements. Clin Neurophysiol. 2021;132(1):232–45.
Xing X, Liang G, Zhang Y, Khanal S, Lin A-L, Jacobs N. Advit: Vision transformer on multi-modality pet images for alzheimer disease diagnosis. In: IEEE 19th International symposium on biomedical imaging (ISBI). IEEE. 2022;2022:1–4.
Tummala S, Kadry S, Bukhari SAC, Rauf HT. Classification of brain tumor from magnetic resonance imaging using vision transformers ensembling. Curr Oncol. 2022;29(10):7498–511.
Carcagnì P, Leo M, Del Coco M, Distante C, De Salve A. Convolution neural networks and self-attention learners for Alzheimer dementia diagnosis from brain MRI. Sensors. 2023;23(3):1694.
Dai Z, Yi J, Yan L, Xu Q, Hu L, Zhang Q, Li J, Wang G. PFEMed: few-shot medical image classification using prior guided feature enhancement. Pattern Recogn. 2023;134.
Wang X, Yuan Y, Guo D, Huang X, Cui Y, Xia M, Wang Z, Bai C, Chen S. SSA-Net: spatial self-attention network for COVID-19 pneumonia infection segmentation with semi-supervised few-shot learning. Med Image Anal. 2022;79.
Tong T, Wolz R, Gao Q, Guerrero R, Hajnal JV, Rueckert D, Initiative ADN, et al. Multiple instance learning for classification of dementia in brain MRI. Med Image Anal. 2014;18(5):808–18.
Atnafu SW, Diciotti S. Development of an interpretable deep learning system for the identification of patients with Alzheimer’s disease. 2022
Liu S, Masurkar AV, Rusinek H, Chen J, Zhang B, Zhu W, Fernandez-Granda C, Razavian N. Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs. Sci Rep. 2022;12(1):17106.
Noella RN, Priyadarshini J. Diagnosis of Alzheimer’s, Parkinson’s disease and frontotemporal dementia using a generative adversarial deep convolutional neural network. Neural Comput Appl. 2023;35(3):2845–54.
Yadav BK, Hashmi MF .: An attention-based CNN architecture for Alzheimer’s classification and detection. In: 2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET), IEEE, 2023; 1–5
Shi B, Chen Y, Zhang P, Smith CD, Liu J, Initiative ADN, et al. Nonlinear feature transformation and deep fusion for Alzheimer’s disease staging analysis. Pattern Recogn. 2017;63:487–98.
Venugopalan J, Tong L, Hassanzadeh HR, Wang MD. Multimodal deep learning models for early detection of Alzheimer’s disease stage. Sci Rep. 2021;11(1):3254.
Allioui H, Sadgal M, Elfazziki A. Deep MRI segmentation: a convolutional method applied to Alzheimer disease detection. Int. J. Adv. Comput. Sci. Appl. 10;11
Mishra D, Singh SK, Singh RK. Lossy medical image compression using residual learning-based dual autoencoder model. In: IEEE 7th Uttar Pradesh section international conference on electrical, electronics and computer engineering (UPCON). IEEE. 2020;2020:1–5.
Farshad A, Yeganeh Y, Gehlbach P, Navab N. Y-net: a spatiospectral dual-encoder network for medical image segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer, 2022;582–592
Isola P, Zhu J-Y, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017;1125–1134
Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks 2015. arXiv preprint arXiv:1511.06434
Mehmood A, Maqsood M, Bashir M, Shuyuan Y. A deep Siamese convolution neural network for multi-class classification of Alzheimer disease. Brain Sci. 2020;10(2):84.
Cui R, Liu M, Initiative ADN, et al. RNN-based longitudinal analysis for diagnosis of Alzheimer’s disease. Comput Med Imaging Graph. 2019;73:1–10.
Hong X, Lin R, Yang C, Zeng N, Cai C, Gou J, Yang J. Predicting Alzheimer’s disease using LSTM. Ieee Access. 2019;7:80893–901.
Faturrahman M, Wasito I, Hanifah N, Mufidah R. Structural mri classification for alzheimer’s disease detection using deep belief network. In: 11th International conference on information & communication technology and system (ICTS). IEEE. 2017;2017:37–42.
Yagis E, Atnafu SW, Herrera A, Marzi C, Scheda R, Giannelli M, Tessa C, Citi L, Diciotti S. Effect of data leakage in brain MRI classification using 2d convolutional neural networks. Sci Rep. 2021;11(1):22544.
Biswas R, Gini RJ. Multi-class classification of Alzheimer’s disease detection from 3d MRI image using ml techniques and its performance analysis. Multimed Tool Appl. 2024;83(11):33527–54.
Ávila-Jiménez JL, et al. A deep learning model for Alzheimer’s disease diagnosis based on patient clinical records. Comput Biol Med. 2024;169.
Mercaldo F, et al. Triad: a deep ensemble network for Alzheimer classification and localisation. IEEE Access. 2023
Shin H, et al. Vision transformer approach for classification of Alzheimer’s disease using 18F-Florbetaben brain images. Appl Sci. 2023;13(6):3453.
Lee M-W, et al. A multimodal machine learning model for predicting dementia conversion in Alzheimer’s disease. Sci Rep. 2024;14(1):12276.
Nguyen H, Dang HB, Dao PN. On-policy and off-policy q-learning strategies for spacecraft systems: an approach for time-varying discrete-time without controllability assumption of augmented system. Aerosp Sci Technol. 2024;146.
Wang J, Liu J, Zheng Y, Zhang D. Data-based l2 gain optimal control for discrete-time system with unknown dynamics. J Franklin Inst. 2023;360(6):4354–77.
Article MathSciNet Google Scholar
Raj A, Mirzaei G. Reinforcement-learning-based localization of hippocampus for Alzheimer’s disease detection. Diagnostics. 2023;13(21):3292.
Dhanusha C, Kumar AS. Deep recurrent q reinforcement learning model to predict the Alzheimer disease using smart home sensor data. In: IOP Conference series: materials science and engineering. IOP Publishing, 2021
Schwartz E, Karlinsky L, Shtok J, Harary S, Marder M, Kumar A, Feris R, Giryes R, Bronstein A. \(\delta \)-encoder: an effective sample synthesis method for few-shot object recognition. In: Annual conference on neural information processing systems. Neural Inform. Process. Syst. Found. 2018
Gao H, Shou Z, Zareian A, Zhang H, Chang S-F. Low-shot learning via covariance-preserving adversarial augmentation networks. Adv. Neural Inform. Process. Syst. 2018;31
Ravi S, Larochelle H. Optimization as a model for few-shot learning. In: International conference on learning representations. 2017
He K, Zhang X, Ren S, Sun J .: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016;770–778
Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18, Springer, 2015;234–241
Qian L, Zhou X, Li Y, Hu Z. Unet#: a unet-like redesigning skip connections for medical image segmentation 2022. arXiv preprint arXiv:2205.11759
Liu Y, Wang H, Chen Z, Huangliang K, Zhang H. Transunet+: redesigning the skip connection to enhance features in medical image segmentation. Knowl-Based Syst. 2022;256.
Sokolova M, Japkowicz N, Szpakowicz S. Beyond accuracy, f-score and roc: a family of discriminant measures for performance evaluation. In: Australasian joint conference on artificial intelligence, Springer, 2006;1015–1021
留言 (0)