Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Yao, Lanhong"'
Autor:
Zhang, Zheyuan, Keles, Elif, Durak, Gorkem, Taktak, Yavuz, Susladkar, Onkar, Gorade, Vandan, Jha, Debesh, Ormeci, Asli C., Medetalibeyoglu, Alpay, Yao, Lanhong, Wang, Bin, Isler, Ilkin Sevgi, Peng, Linkai, Pan, Hongyi, Vendrami, Camila Lopes, Bourhani, Amir, Velichko, Yury, Gong, Boqing, Spampinato, Concetto, Pyrros, Ayis, Tiwari, Pallavi, Klatte, Derk C. F., Engels, Megan, Hoogenboom, Sanne, Bolan, Candice W., Agarunov, Emil, Harfouch, Nassier, Huang, Chenchan, Bruno, Marco J., Schoots, Ivo, Keswani, Rajesh N., Miller, Frank H., Gonda, Tamas, Yazici, Cemal, Tirkes, Temel, Turkbey, Baris, Wallace, Michael B., Bagci, Ulas
Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, la
Externí odkaz:
http://arxiv.org/abs/2405.12367
Autor:
Zhang, Zheyuan, Yao, Lanhong, Wang, Bin, Jha, Debesh, Keles, Elif, Medetalibeyoglu, Alpay, Bagci, Ulas
Large-scale, big-variant, and high-quality data are crucial for developing robust and successful deep-learning models for medical applications since they potentially enable better generalization performance and avoid overfitting. However, the scarcit
Externí odkaz:
http://arxiv.org/abs/2310.12868
Autor:
Yao, Lanhong, Zhang, Zheyuan, Demir, Ugur, Keles, Elif, Vendrami, Camila, Agarunov, Emil, Bolan, Candice, Schoots, Ivo, Bruno, Marc, Keswani, Rajesh, Miller, Frank, Gonda, Tamas, Yazici, Cemal, Tirkes, Temel, Wallace, Michael, Spampinato, Concetto, Bagci, Ulas
Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning an
Externí odkaz:
http://arxiv.org/abs/2309.05857
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. The combination of different neural architectures is a
Externí odkaz:
http://arxiv.org/abs/2308.00128
Autor:
Zhang, Zheyuan, Wang, Bin, Yao, Lanhong, Demir, Ugur, Jha, Debesh, Turkbey, Ismail Baris, Gong, Boqing, Bagci, Ulas
Most statistical learning algorithms rely on an over-simplified assumption, that is, the train and test data are independent and identically distributed. In real-world scenarios, however, it is common for models to encounter data from new and differe
Externí odkaz:
http://arxiv.org/abs/2304.02720
Publikováno v:
In Advances in Clinical Radiology
Akademický článek
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Autor:
Yao L; Department of Radiology, Northwestern University, Chicago IL 60611, USA., Zhang Z; Department of Radiology, Northwestern University, Chicago IL 60611, USA., Demir U; Department of Radiology, Northwestern University, Chicago IL 60611, USA., Keles E; Department of Radiology, Northwestern University, Chicago IL 60611, USA., Vendrami C; Department of Radiology, Northwestern University, Chicago IL 60611, USA., Agarunov E; NYU Langone Health, New York, NY 10016., Bolan C; Mayo Clinic, Rochester, MN 55905., Schoots I; Erasmus Medical Center, 3015 GD Rotterdam, Netherlands., Bruno M; Erasmus Medical Center, 3015 GD Rotterdam, Netherlands., Keswani R; Department of Radiology, Northwestern University, Chicago IL 60611, USA., Miller F; Department of Radiology, Northwestern University, Chicago IL 60611, USA., Gonda T; NYU Langone Health, New York, NY 10016., Yazici C; University of Illinois Chicago, Chicago, IL 60607., Tirkes T; Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202., Wallace M; Sheikh Shakhbout Medical City, 11001, Abu Dhabi, United Arab Emirates., Spampinato C; University of Catania, 95124 Catania CT, Italy., Bagci U; Department of Radiology, Northwestern University, Chicago IL 60611, USA.
Publikováno v:
Machine learning in medical imaging. MLMI (Workshop) [Mach Learn Med Imaging] 2023 Oct; Vol. 14349, pp. 134-143. Date of Electronic Publication: 2023 Oct 15.