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.

Xehetasun bibliografikoak
Egile Nagusiak:	Siddiki Shan, Md. Abdul Kahhar, Quaiyum, Md. Abdul, Saha, Sugata, Nayer Anik, S. M. Navin
Beste egile batzuk:	Rabiul Alam, Dr. Md. Golam
Formatua:	Thesis
Hizkuntza:	English
Argitaratua:	Brac University 2023
Gaiak:	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
Sarrera elektronikoa:	http://hdl.handle.net/10361/19232

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