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.

Détails bibliographiques
Auteurs principaux: Siddiki Shan, Md. Abdul Kahhar, Quaiyum, Md. Abdul, Saha, Sugata, Nayer Anik, S. M. Navin
Autres auteurs: Rabiul Alam, Dr. Md. Golam
Format: Thèse
Langue:English
Publié: Brac University 2023
Sujets:
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
Accès en ligne:http://hdl.handle.net/10361/19232

Internet

http://hdl.handle.net/10361/19232

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