Optimal transport theory based GAN for medical image augmentation and classification

Optimal transport theory based GAN for medical image augmentation and classification

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Dettagli Bibliografici
Autori principali:	Siddiki Shan, Md. Abdul Kahhar, Quaiyum, Md. Abdul, Saha, Sugata, Nayer Anik, S. M. Navin
Altri autori:	Rabiul Alam, Dr. Md. Golam
Natura:	Tesi
Lingua:	English
Pubblicazione:	Brac University 2023
Soggetti:	Optimal Transport(OT) Theory Generative Adversarial Networks (GANs) Unsupervised neural networks Wasserstein distance Maximum Mean Discrepancy(MMD) Chest X-Ray Covid-19 Viral pneumonia Lung opacity Natural language processing (Computer science) Machine learning
Accesso online:	http://hdl.handle.net/10361/19232

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