Zobrazeno 1 - 10
of 684
pro vyhledávání: '"Yoon, Jee In"'
Medical image analysis (MedIA) has become an essential tool in medicine and healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in deep learning (DL) have made significant contributions to its advances. Ho
Externí odkaz:
http://arxiv.org/abs/2310.08598
Publikováno v:
Proceedings of Information Processing in Medical Imaging, 2023, pp. 388-400
Human organs constantly undergo anatomical changes due to a complex mix of short-term (e.g., heartbeat) and long-term (e.g., aging) factors. Evidently, prior knowledge of these factors will be beneficial when modeling their future state, i.e., via im
Externí odkaz:
http://arxiv.org/abs/2212.08228
Publikováno v:
Heliyon, Vol 10, Iss 19, Pp e38637- (2024)
Ponatinib is a potent tyrosine kinase inhibitor that is approved for the treatment of chronic myeloid leukemia and Philadelphia chromosome-positive acute lymphoblastic leukemia. To further expand its clinical applications, accurate quantification of
Externí odkaz:
https://doaj.org/article/cf48f828544f4649ac7a51d7e42bfd22
Diagnosing Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristics make AD biomarker identification from structural brain imaging (e.g.
Externí odkaz:
http://arxiv.org/abs/2207.13223
Publikováno v:
In Heliyon 15 October 2024 10(19)
Publikováno v:
In Biomedicine & Pharmacotherapy September 2024 178
Publikováno v:
In Journal of Photochemistry & Photobiology, B: Biology December 2024 261
Autor:
Kim, Jae Rim, Park, Jung-A, Kim, Hong-Jik, Yoon, Jee-Eun, Oh, Dana, Park, Hyo Jin, Paik, Sang Min, Lee, Woo-Jin, Kim, Daeyoung, Yang, Kwang Ik, Chu, Min Kyung, Yun, Chang-Ho
Publikováno v:
In Sleep Medicine December 2024 124:371-377
Existing studies on disease diagnostic models focus either on diagnostic model learning for performance improvement or on the visual explanation of a trained diagnostic model. We propose a novel learn-explain-reinforce (LEAR) framework that unifies d
Externí odkaz:
http://arxiv.org/abs/2108.09451
Publikováno v:
In Toxicology Letters April 2024 394:57-65