{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T01:10:53Z","timestamp":1774746653711,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is a progressive, non-curable neurodegenerative disorder that poses persistent challenges for early diagnosis due to its gradual onset and the difficulty in distinguishing pathological changes from normal aging. Neuroimaging, particularly MRI and PET, plays a key role in detection; however, limitations in data availability and the complexity of early structural biomarkers constrain traditional diagnostic approaches. This review investigates the use of generative models, specifically Generative Adversarial Networks (GANs) and Diffusion Models, as emerging tools to address these challenges. These models are capable of generating high-fidelity synthetic brain images, augmenting datasets, and enhancing machine learning performance in classification tasks. The review synthesizes findings across multiple studies, revealing that GAN-based models achieved diagnostic accuracies up to 99.70%, with image quality metrics such as SSIM reaching 0.943 and PSNR up to 33.35 dB. Diffusion Models, though relatively new, demonstrated strong performance with up to 92.3% accuracy and FID scores as low as 11.43. Integrating generative models with convolutional neural networks (CNNs) and multimodal inputs further improved diagnostic reliability. Despite these advancements, challenges remain, including high computational demands, limited interpretability, and ethical concerns regarding synthetic data. This review offers a comprehensive perspective to inform future AI-driven research in early AD detection.<\/jats:p>","DOI":"10.3390\/a18070434","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T15:01:05Z","timestamp":1752591665000},"page":"434","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Early Detection of Alzheimer\u2019s Disease Using Generative Models: A Review of GANs and Diffusion Models in Medical Imaging"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2148-5786","authenticated-orcid":false,"given":"Md Minul","family":"Alam","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV 89154, USA"}]},{"given":"Shahram","family":"Latifi","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV 89154, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2024). Ageing and Health, WHO (World Health Organization)."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"199","DOI":"10.2147\/JIR.S86958","article-title":"Targeting neuroinflammation in Alzheimer\u2019s disease","volume":"9","author":"Bronzuoli","year":"2016","journal-title":"J. Inflamm. Res."},{"key":"ref_3","unstructured":"Alzheimer\u2019s Disease International (2024). Alzheimer\u2019s Disease International, Alzheimer\u2019s Disease International."},{"key":"ref_4","unstructured":"Alzheimer and Association (2025). American Perspectives on Early Detection of Alzheimer\u2019s Disease in the Era of Treatment, Alzheimer\u2019s Association."},{"key":"ref_5","first-page":"76","article-title":"Factors Contributing to Alzheimer\u2019s disease in Older Adult Populations: A Narrative Review","volume":"10","author":"Uddin","year":"2024","journal-title":"Int. J. Sci. Res. Multidiscip. Stud."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/BF00308809","article-title":"Neuropathological stageing of Alzheimer-related changes","volume":"82","author":"Braak","year":"1991","journal-title":"Acta Neuropathol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"886619","DOI":"10.3389\/fpsyg.2022.886619","article-title":"Brain Structural and Functional Changes in Cognitive Impairment Due to Alzheimer\u2019s Disease","volume":"13","author":"Dolado","year":"2022","journal-title":"Front. Psychol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","article-title":"NIA-AA Research Framework: Toward a biological definition of Alzheimer\u2019s disease","volume":"14","author":"Jack","year":"2018","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_9","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative Adversarial Networks. arXiv."},{"key":"ref_10","unstructured":"Bowles, C., Chen, L., Guerrero, R., Bentley, P., Gunn, R., Hammers, A., Dickie, D.A., Hern\u00e1ndez, M.V., Wardlaw, J., and Rueckert, D. (2018). GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1109\/TMI.2017.2764326","article-title":"Multimodal MR Synthesis via Modality-Invariant Latent Representation","volume":"37","author":"Chartsias","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/TMI.2019.2922960","article-title":"CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)","volume":"39","author":"You","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_13","unstructured":"Ho, J., Jain, A., and Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. arXiv."},{"key":"ref_14","first-page":"1","article-title":"Importance of Psychological Well-being after disasters in Bangladesh: A Narrative Review","volume":"10","author":"Khandoker","year":"2024","journal-title":"Int. J. Sci. Res. Multidiscip. Stud."},{"key":"ref_15","unstructured":"Clinic, M. (2025, July 04). Alzheimer\u2019s Stages: How the Disease Progresses. Available online: https:\/\/www.mayoclinic.org\/diseases-conditions\/alzheimers-disease\/in-depth\/alzheimers-stages\/art-20048448."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/RBME.2018.2886237","article-title":"Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects","volume":"12","author":"Ahmed","year":"2019","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/S1474-4422(02)00002-9","article-title":"Structural magnetic resonance imaging in the practical assessment of dementia: Beyond exclusion","volume":"1","author":"Scheltens","year":"2002","journal-title":"Lancet Neurol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kim, J., Jeong, M., Stiles, W.R., and Choi, H.S. (2022). Neuroimaging Modalities in Alzheimer\u2019s Disease: Diagnosis and Clinical Features. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23116079"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"10062","DOI":"10.21037\/apm-21-825","article-title":"A literature review of MRI techniques used to detect amyloid-beta plaques in Alzheimer\u2019s disease patients","volume":"10","author":"Yu","year":"2021","journal-title":"Ann. Palliat. Med."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Alves, G.S., Kn\u00f6chel, V.O., Kn\u00f6chel, C., Carvalho, A.F., Pantel, J., Engelhardt, E., and Laks, J. (2015). Integrating Retrogenesis Theory to Alzheimer\u2019s Disease Pathology: Insight from DTI-TBSS Investigation of the White Matter Microstructural Integrity. Biomed. Res. Int., 2015.","DOI":"10.1155\/2015\/291658"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1016\/j.neuron.2011.12.040","article-title":"A Network Diffusion Model of Disease Progression in Dementia","volume":"73","author":"Raj","year":"2012","journal-title":"Neuron"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1016\/j.compbiomed.2013.07.004","article-title":"Classification of diffusion tensor images for the early detection of Alzheimer\u2019s disease","volume":"43","author":"Lee","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Pan, Y., Liu, M., Lian, C., Zhou, T., Xia, Y., and Shen, D. (2018). Synthesizing missing PET from MRI with cycle-consistent generative adversarial networks for Alzheimer\u2019s disease diagnosis. Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2018, Proceedings of the 21st International Conference, Granada, Spain, 16\u201320 September 2018, Springer. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).","DOI":"10.1007\/978-3-030-00931-1_52"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Han, C., Rundo, L., Murao, K., Milacski, Z.\u00c1., Umemoto, K., Sala, E., Nakayama, H., and Satoh, S. (2019). GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer\u2019s Disease Diagnosis. arXiv.","DOI":"10.1007\/978-3-030-63061-4_5"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shin, H.-C., Ihsani, A., Xu, Z., Mandava, S., Sreenivas, S.T., Forster, C., Cha, J., and Alzheimer\u2019s Disease Neuroimaging Initiative (2020). GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-Tuning for Alzheimer\u2019s Disease Diagnosis from MRI. Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020, Proceedings of the 23rd International Conference, Lima, Peru, 4\u20138 October 2020, Springer Science and Business Media Deutschland GmbH. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).","DOI":"10.1007\/978-3-030-59713-9_66"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s40708-020-00104-2","article-title":"GAN-based synthetic brain PET image generation","volume":"7","author":"Islam","year":"2020","journal-title":"Brain Inf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hu, S., Shen, Y., Wang, S., and Lei, B. (2020). Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network. Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020, Proceedings of the 23rd International Conference, Lima, Peru, 4\u20138 October 2020, Springer Science and Business Media Deutschland GmbH. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).","DOI":"10.1007\/978-3-030-59713-9_67"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hu, S., Yu, W., Chen, Z., and Wang, S. (2020, January 11\u201314). Medical Image Reconstruction Using Generative Adversarial Network for Alzheimer Disease Assessment with Class-Imbalance Problem. Proceedings of the 2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020, Chengdu, China.","DOI":"10.1109\/ICCC51575.2020.9344912"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1109\/JBHI.2020.3006925","article-title":"Prediction of Alzheimer\u2019s Disease Progression with Multi-Information Generative Adversarial Network","volume":"25","author":"Zhao","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Han, C., Rundo, L., Murao, K., Noguchi, T., Shimahara, Y., Milacski, Z.A., Koshino, S., Sala, E., Nakayama, H., and Satoh, S. (2021). MADGAN: Unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction. BMC Bioinform., 22.","DOI":"10.1186\/s12859-020-03936-1"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s13195-021-00797-5","article-title":"Enhancing magnetic resonance imaging-driven Alzheimer\u2019s disease classification performance using generative adversarial learning","volume":"13","author":"Zhou","year":"2021","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/JBHI.2021.3097721","article-title":"Task-Induced Pyramid and Attention GAN for Multimodal Brain Image Imputation and Classification in Alzheimer\u2019s Disease","volume":"26","author":"Gao","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_33","unstructured":"Yu, W., Lei, B., Ng, M.K., Cheung, A.C., Shen, Y., and Wang, S. (2020). Tensorizing GAN with High-Order Pooling for Alzheimer\u2019s Disease Assessment. arXiv."},{"key":"ref_34","unstructured":"Pan, J., and Wang, S. (2022). Cross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer\u2019s Disease. arXiv."},{"key":"ref_35","first-page":"9","article-title":"Wasserstein Gan-Gradient Penalty with Deep Transfer Learning Based Alzheimer Disease Classification on 3D Mri Scans","volume":"9","author":"Thota","year":"2022","journal-title":"I-Manag. J. Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106676","DOI":"10.1016\/j.cmpb.2022.106676","article-title":"BPGAN: Brain PET synthesis from MRI using generative adversarial network for multi-modal Alzheimer\u2019s disease diagnosis","volume":"217","author":"Zhang","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yuan, C., Duan, J., Tustison, N.J., Xu, K., Hubbard, R.A., and Linn, K.A. (2023). ReMiND: Recovery of Missing Neuroimaging using Diffusion Models with Application to Alzheimer\u2019s Disease. medRxiv.","DOI":"10.1101\/2023.08.16.23294169"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Huang, G., Chen, X., Shen, Y., and Wang, S. (2023). MR Image Super-Resolution Using Wavelet Diffusion for Predicting Alzheimer\u2019s Disease. Brain Informatics, Proceedings of the 16th International Conference, BI 2023, Hoboken, NJ, USA, 1\u20133 August 2023, Springer Science and Business Media Deutschland GmbH. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).","DOI":"10.1007\/978-3-031-43075-6_13"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Boyapati, N., Tej, M.B., Darshitha, M., Shreya, P., Naveen, S.N., Gandhi, C.R., and Amrutha, V. (2023, January 21\u201323). Alzheimer\u2019s Disease Prediction using Convolutional Neural Network (CNN) with Generative Adversarial Network (GAN). Proceedings of the 2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023, Chennai, India.","DOI":"10.1109\/ICDSAAI59313.2023.10452539"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102636","DOI":"10.1016\/j.artmed.2023.102636","article-title":"Deep grading for MRI-based differential diagnosis of Alzheimer\u2019s disease and Frontotemporal dementia","volume":"144","author":"Nguyen","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sekhar, U.S., Vyas, N., Dutt, V., and Kumar, A. (2023, January 10\u201311). Multimodal Neuroimaging Data in Early Detection of Alzheimer\u2019s Disease: Exploring the Role of Ensemble Models and GAN Algorithm. Proceedings of the International Conference on Circuit Power and Computing Technologies, ICCPCT 2023, Kollam, India.","DOI":"10.1109\/ICCPCT58313.2023.10245177"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"102654","DOI":"10.1016\/j.artmed.2023.102654","article-title":"Real-world prediction of preclinical Alzheimer\u2019s disease with a deep generative model","volume":"144","author":"Hwang","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yao, W., Shen, Y., Nicolls, F., and Wang, S.Q. (2023). Conditional Diffusion Model-Based Data Augmentation for Alzheimer\u2019s Prediction. Communications in Computer and Information Science, Springer Science and Business Media Deutschland GmbH.","DOI":"10.1007\/978-981-99-5844-3_3"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"120663","DOI":"10.1016\/j.neuroimage.2024.120663","article-title":"Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer\u2019s disease classification","volume":"296","author":"Yoon","year":"2024","journal-title":"Neuroimage"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.procs.2024.08.021","article-title":"Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer\u2019s Disease Classification","volume":"241","author":"Tufail","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1443151","DOI":"10.3389\/fmed.2024.1443151","article-title":"DeepCGAN: Early Alzheimer\u2019s detection with deep convolutional generative adversarial networks","volume":"11","author":"Ali","year":"2024","journal-title":"Front. Med."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Shah, J., Che, Y., Sohankar, J., Luo, J., Li, B., Su, Y., Wu, T., and For the Alzheimer\u2019s Disease Neuroimaging Initiative (2024). Enhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models. Life, 14.","DOI":"10.20944\/preprints202411.0051.v1"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e14421","DOI":"10.1002\/alz.14421","article-title":"A multi-view learning approach with diffusion model to synthesize FDG PET from MRI T1WI for diagnosis of Alzheimer\u2019s disease","volume":"21","author":"Chen","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Dhinagar, N.J., Thomopoulos, S.I., Laltoo, E., and Thompson, P.M. (2024, January 15\u201319). Counterfactual MRI Generation with Denoising Diffusion Models for Interpretable Alzheimer\u2019s Disease Effect Detection. Proceedings of the 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC53108.2024.10782737"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"131231","DOI":"10.1109\/ACCESS.2024.3354724","article-title":"An Empirical Analysis of Diffusion, Autoencoders, and Adversarial Deep Learning Models for Predicting Dementia Using High-Fidelity MRI","volume":"12","author":"Gajjar","year":"2024","journal-title":"IEEE Access"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"17037","DOI":"10.1038\/s41598-024-66874-5","article-title":"Exceptional performance with minimal data using a generative adversarial network for alzheimer\u2019s disease classification","volume":"14","author":"Wong","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Shou, Q., Cen, S., Chen, N.-K., Ringman, J.M., Wen, J., Kim, H., Wang, D.J., and Alzheimer\u2019s Disease Neuroimaging Initiative (2024). Diffusion model enables quantitative CBF analysis of Alzheimer\u2019s Disease. medRxiv.","DOI":"10.1101\/2024.07.01.24309791"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Hwang, S., and Shin, J. (2024, January 3\u20135). Prognosis Prediction of Alzheimer\u2019s Disease Based on Multi-Modal Diffusion Model. Proceedings of the 2024 18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IMCOM60618.2024.10418423"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"191","DOI":"10.37934\/araset.45.2.191201","article-title":"Performance Enhancement of Alzheimer\u2019s Disease Diagnosis Using Generative Adversarial Network","volume":"45","author":"Ching","year":"2025","journal-title":"J. Adv. Res. Appl. Sci. Eng. Technol."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Wang, L. (2024). Early Diagnosis of Alzheimer\u2019s Disease Using Dual GAN Model with Pyramid Attention Networks, Taylor and Francis Ltd.","DOI":"10.1080\/09540091.2024.2321351"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ou, Z., Jiang, C., Pan, Y., Zhang, Y., Cui, Z., and Shen, D. (2024, January 3\u20139). A Prior-information-guided Residual Diffusion Model for Multi-modal PET Synthesis from MRI. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence Main Track, Jeju-si, Republic of Korea.","DOI":"10.24963\/ijcai.2024\/527"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"12727","DOI":"10.1038\/s41598-025-94677-9","article-title":"Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images","volume":"15","author":"Oraby","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhou, T., Ding, C., Jing, C., Liu, F., Hung, K., Pham, H., Mahmud, M., Lyu, Z., Qiao, S., and Wang, S. (2025). BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer\u2019s Disease. IEEE Trans. Consum. Electron.","DOI":"10.1109\/TCE.2025.3543943"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Gavidia-Bovadilla, G., Kanaan-Izquierdo, S., Mataroa-Serrat, M., and Perera-Lluna, A. (2017). Early prediction of Alzheimer\u2019s disease using null longitudinal model-based classifiers. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0168011"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.media.2017.10.005","article-title":"Landmark-based deep multi-instance learning for brain disease diagnosis","volume":"43","author":"Liu","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_61","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv."},{"key":"ref_62","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional Generative Adversarial Nets. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"101938","DOI":"10.1016\/j.artmed.2020.101938","article-title":"GANs for medical image analysis","volume":"109","author":"Kazeminia","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 15\u201320). A Style-Based Generator Architecture for Generative Adversarial Networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_65","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein Generative Adversarial Networks. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_66","unstructured":"Song, Y., and Ermon, S. (2019). Generative Modeling by Estimating Gradients of the Data Distribution. arXiv."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/7\/434\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:10:04Z","timestamp":1760033404000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/7\/434"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,15]]},"references-count":66,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["a18070434"],"URL":"https:\/\/doi.org\/10.3390\/a18070434","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,15]]}}}