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.

Bibliografische gegevens
Hoofdauteurs: Siddiki Shan, Md. Abdul Kahhar, Quaiyum, Md. Abdul, Saha, Sugata, Nayer Anik, S. M. Navin
Andere auteurs: Rabiul Alam, Dr. Md. Golam
Formaat: Thesis
Taal:English
Gepubliceerd in: Brac University 2023
Onderwerpen:
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
Online toegang:http://hdl.handle.net/10361/19232

Internet

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

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