1 code implementation • 20 May 2024 • Zheyuan Zhang, Elif Keles, Gorkem Durak, Yavuz Taktak, Onkar Susladkar, Vandan Gorade, Debesh Jha, Asli C. Ormeci, Alpay Medetalibeyoglu, Lanhong Yao, Bin Wang, Ilkin Sevgi Isler, Linkai Peng, Hongyi Pan, Camila Lopes Vendrami, Amir Bourhani, Yury Velichko, Boqing Gong, Concetto Spampinato, Ayis Pyrros, Pallavi Tiwari, Derk C. F. Klatte, Megan Engels, Sanne Hoogenboom, Candice W. Bolan, Emil Agarunov, Nassier Harfouch, Chenchan Huang, Marco J. Bruno, Ivo Schoots, Rajesh N. Keswani, Frank H. Miller, Tamas Gonda, Cemal Yazici, Temel Tirkes, Baris Turkbey, Michael B. Wallace, Ulas Bagci
We also collected CT scans of 1, 350 patients from publicly available sources for benchmarking purposes.
no code implementations • 19 Oct 2023 • Zheyuan Zhang, Lanhong Yao, Bin Wang, Debesh Jha, Elif Keles, Alpay Medetalibeyoglu, Ulas Bagci
We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data that preserve the essential characteristics of the original medical images by incorporating edge information of objects to guide the synthesis process.
no code implementations • 11 Sep 2023 • Lanhong Yao, Zheyuan Zhang, Ugur Demir, Elif Keles, Camila Vendrami, Emil Agarunov, Candice Bolan, Ivo Schoots, Marc Bruno, Rajesh Keswani, Frank Miller, Tamas Gonda, Cemal Yazici, Temel Tirkes, Michael Wallace, Concetto Spampinato, Ulas Bagci
We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field.
no code implementations • 31 Jul 2023 • Lanhong Yao, Zheyuan Zhang, Ulas Bagci
Brain tumor segmentation is an active research area due to the difficulty in delineating highly complex shaped and textured tumors as well as the failure of the commonly used U-Net architectures.
1 code implementation • 5 Apr 2023 • Zheyuan Zhang, Bin Wang, Lanhong Yao, Ugur Demir, Debesh Jha, Ismail Baris Turkbey, Boqing Gong, Ulas Bagci
In real-world scenarios, however, it is common for models to encounter data from new and different domains to which they were not exposed to during training.