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.

Bibliografiset tiedot
Päätekijät: Siddiki Shan, Md. Abdul Kahhar, Quaiyum, Md. Abdul, Saha, Sugata, Nayer Anik, S. M. Navin
Muut tekijät: Rabiul Alam, Dr. Md. Golam
Aineistotyyppi: Opinnäyte
Kieli:English
Julkaistu: Brac University 2023
Aiheet:
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
Linkit:http://hdl.handle.net/10361/19232

Internet

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

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