Lee J-G, Jun S, Cho Y-W, Lee H, Kim GB, Seo JB, et al. Deep learning in medical imaging: general overview. Korean J Radiol. 2017;18:570–84.

Article  PubMed  PubMed Central  Google Scholar 

Sarvamangala DR, Kulkarni RV. Convolutional neural networks in medical image understanding: a survey. Evol Intel. 2022;15:1–22.

Article  CAS  Google Scholar 

Arabi H, AkhavanAllaf A, Sanaat A, Shiri I, Zaidi H. The promise of artificial intelligence and deep learning in PET and SPECT imaging. Physica Medica. 2021;83:122–37.

Article  PubMed  Google Scholar 

Lee R, Shin JH, Choi H, Kim H-J, Cheon GJ, Jeon B. Variability of FP-CIT PET patterns associated with clinical features of multiple system atrophy. Neurology. 2021;96:e1663–71.

Article  CAS  PubMed  Google Scholar 

Choi H, Kim YK, Yoon EJ, Lee J-Y. Lee DS, for the Alzheimer’s Disease Neuroimaging Initiative. Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer’s disease to Parkinson’s disease. Eur J Nucl Med Mol Imaging. 2020;47:403–12.

Article  PubMed  Google Scholar 

Hamdi M, Bourouis S, Rastislav K, Mohmed F. Evaluation of neuro images for the diagnosis of Alzheimer’s disease using deep learning neural network. Front Public Health. 2022;10:834032.

Article  PubMed  PubMed Central  Google Scholar 

Lai Y-C, Wu K-C, Tseng N-C, Chen Y-J, Chang C-J, Yen K-Y, et al. Differentiation between malignant and benign pulmonary nodules by using automated three-dimensional high-resolution representation learning with fluorodeoxyglucose positron emission tomography-computed tomography. Frontiers in Medicine. 2022;9:773041.

Article  PubMed  PubMed Central  Google Scholar 

Capobianco N, Meignan M, Cottereau A-S, Vercellino L, Sibille L, Spottiswoode B, et al. Deep-Learning 18F-FDG uptake classification enables total metabolic tumor volume estimation in diffuse large B-cell lymphoma. J Nucl Med. 2021;62:30–6.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kawauchi K, Furuya S, Hirata K, Katoh C, Manabe O, Kobayashi K, et al. A convolutional neural network-based system to classify patients using FDG PET/CT examinations. BMC Cancer. 2020;20:227.

Article  PubMed  PubMed Central  Google Scholar 

Morid MA, Borjali A, Del Fiol G. A scoping review of transfer learning research on medical image analysis using ImageNet. Comput Biol Med. 2021;128:104115.

Article  PubMed  Google Scholar 

Whi W, Choi H, Paeng JC, Cheon GJ, Kang KW, Lee DS. Fully automated identification of brain abnormality from whole-body FDG-PET imaging using deep learning-based brain extraction and statistical parametric mapping. EJNMMI Phys. 2021;8:79.

Article  PubMed  PubMed Central  Google Scholar 

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2016:770–8.

Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv. 2016;1603.04467.

van der Maaten L. Learning a parametric embedding by preserving local structure. Proc Twelfth Int Conf Artif Intell Statistics. 2009:384–91.

Shreve PD, Anzai Y, Wahl RL. Pitfalls in oncologic diagnosis with FDG PET imaging: physiologic and benign variants. RadioGraphics. 1999;19:61–77.

Article  CAS  PubMed  Google Scholar 

Purohit BS, Ailianou A, Dulguerov N, Becker CD, Ratib O, Becker M. FDG-PET/CT pitfalls in oncological head and neck imaging. Insights Imaging. 2014;5:585–602.

Article  PubMed  PubMed Central  Google Scholar 

Shammas A, Lim R, Charron M. Pediatric FDG PET/CT: physiologic uptake, normal variants, and benign conditions. RadioGraphics. 2009;29:1467–86.

Article  PubMed  Google Scholar 

Amin A, Rosenbaum SJ, Bockisch A. Physiological 18F-FDG uptake by the spinal cord: is it a point of consideration for cancer patients? J Neurooncol. 2012;107:609–15.

Article  CAS  PubMed  Google Scholar 

Chen S, Ma K, Zheng Y. Med3d: transfer learning for 3d medical image analysis. arXiv preprint arXiv. 2019; 1904.00625.

Kirienko M, Sollini M, Silvestri G, Mognetti S, Voulaz E, Antunovic L, et al. Convolutional neural networks promising in lung cancer T-parameter assessment on baseline FDG-PET/CT. Contrast Media Mol Imaging. 2018;6

Sibille L, Seifert R, Avramovic N, Vehren T, Spottiswoode B, Zuehlsdorff S, et al. 18F-FDG PET/CT uptake classification in lymphoma and lung cancer by using deep convolutional neural networks. Radiology. 2020;294:445–52.

Article  PubMed  Google Scholar 

Fujiwara T, Miyake M, Watanuki S, Mejia MA, Itoh M, Fukuda H. Easy detection of tumor in oncologic whole-body PET by projection reconstruction images with maximum intensity projection algorithm. Ann Nucl Med. 1999;13:199–203.

Article  CAS  PubMed  Google Scholar 

Sun Q, Yang Y, Sun J, Yang Z, Zhang J. Using deep learning for content-based medical image retrieval. Medical Imaging 2017: Imaging Informatics for Healthcare. Res Appl. 2017(10138):270–80.

Shamshad, F., Khan, S., Zamir, S. W., Khan, M. H., Hayat, M., Khan, F. S., et al. Transformers in medical imaging: A survey. arXiv preprint arXiv. 2022; 2201.09873.

Singh SP, Wang L, Gupta S, Goli H, Padmanabhan P, Gulyás B. 3D deep learning on medical images: a review. Sensors. 2020;18:5097.

Article  Google Scholar