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.

Bibliografiska uppgifter
Huvudupphovsmän: Siddiki Shan, Md. Abdul Kahhar, Quaiyum, Md. Abdul, Saha, Sugata, Nayer Anik, S. M. Navin
Övriga upphovsmän: Rabiul Alam, Dr. Md. Golam
Materialtyp: Lärdomsprov
Språk:English
Publicerad: Brac University 2023
Ämnen:
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
Länkar:http://hdl.handle.net/10361/19232

Internet

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

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