Page 106 - AN-4-4

Advanced Neurology Diffusion model for brain tumor classification

models for color normalization in digital pathology and modal data augmentation framework for brain tumor
dermatology: Advancing the learning paradigm. Expert Syst segmentation. Sensors (Basel). 2020;20(15):4203.
Appl. 2024;245:123105.
doi: 10.3390/s20154203
doi: 10.1016/j.eswa.2023.123105
46. Mukherjee D, Saha P, Kaplun D, Sinitca A, Sarkar R. Brain
38. Kaur J, Singh AK, Jindal N. Systematic survey on generative tumor image generation using an aggregation of GAN
adversarial networks for brain tumor segmentation models with style transfer. Sci Rep. 2022;12:9380.
and classification. Concurrency Comput Pract Exper. doi: 10.1038/s41598-022-12646-y
2023;35(27):e7850.
47. Azni HM, Afsharchi M, Allahverdi A. Improving brain tumor
doi: 10.1002/cpe.7850
segmentation performance using CycleGAN-based feature
39. Myronenko A. 3D MRI brain tumor segmentation using extraction. Multimed Tools Appl. 2023;82(12):18039-18058.
autoencoder regularization. In: Lecture Notes in Computer doi: 10.1007/s11042-022-14174-3
Science. Vol. 11384. Cham, Switzerland: Springer Nature;
2019. p. 311-320. 48. Kim S, Kim B, Park H. Synthesis of brain tumor MR images for
learning data augmentation. Med Phys. 2020;48(5):2185-2198.
doi: 10.1007/978-3-030-11726-9_28
doi: 10.1002/mp.14701
40. Huang P, Li D, Jiao Z, et al. Common feature learning for
brain tumor MRI synthesis by context-aware generative 49. Saxena D, Cao J. Generative adversarial networks (GANs).
adversarial network. Med Image Anal. 2022;79:102472. ACM Comput Surv. 2021;54(3):1-38.
doi: 10.1016/j.media.2022.102472 doi: 10.1145/3446374
41. Cirillo MD, Abramian D, Eklund A. Vox2Vox: 3D-GAN for 50. Akbar MU, Larsson M, Blystad I, Eklund A. Brain tumor
brain tumour segmentation. In: Lecture Notes in Computer segmentation using synthetic MR images-a comparison of
Science. Vol. 12658. Cham, Switzerland: Springer Nature; GANs and diffusion models. Sci Data. 2024;11:159.
2021. p. 274-284.
doi: 10.1038/s41597-024-03073-x
doi: 10.1007/978-3-030-72084-1_25
51. Dhariwal P, Nichol A. Diffusion models beat GANs on image
42. Aljohani A, Alharbe N. Generating synthetic images for synthesis. Adv Neural Inf Process Syst. 2021;34:8780-8794.
healthcare with novel deep Pix2Pix GAN. Electronics. 52. Onakpojeruo EP, Mustapha MT, Ozsahin DU, Ozsahin I.
2022;11(21):3470.
Enhanced MRI-based brain tumour classification with a
doi: 10.3390/electronics11213470 novel Pix2pix generative adversarial network augmentation
framework. Brain Commun. 2024;6(6):fcae372.
43. Mehmood M, Alshammari N, Alanazi SA, et al. Improved
colorization and classification of intracranial tumor doi: 10.1093/braincomms/fcae372
expanse in MRI images via hybrid scheme of Pix2Pix- 53. Brain Tumor Classification (MRI) Dataset. Kaggle;
cGANs and NASNet-large. J King Saud Univ Comp Inf Sci. 2024. Available from: https://www.kaggle.com/datasets/
2022;34(7):4358-4374.
sartajbhuvaji/brain-tumor-classification-mri [Last accessed
doi: 10.1016/j.jksuci.2022.05.015 on 2024 Mar 13].
44. Narayanan SJ, Anil AS, Ashtikar C, Chunduri S, Saman S. 54. Cheng J. Brain Tumor Dataset. Figshare; 2017. Available
Automated brain tumor segmentation using GAN from: https://figshare.com/articles/dataset/brain_tumor_
augmentation and optimized U-Net. In: Lecture Notes in dataset/1512427/5 [Last accessed on 2024 Mar 13].
Networks and Systems. Vol. 519. Singapore: Springer Nature; 55. Yu J, Oh H, Lee Y, Yang J. Denoising diffusion model with
2023. p. 635-646.
adversarial learning for unsupervised anomaly detection on
doi: 10.1007/978-981-19-5191-6_51 brain MRI images. Pattern Recognit Lett. 2024;186:229-235.
45. Li Q, Yu Z, Wang Y, Zheng H. TumorGAN: A multi- doi: 10.1016/j.patrec.2024.10.007

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