Zobrazeno 1 - 10
of 6 357
pro vyhledávání: '"Computer Aided Detection"'
Autor:
Joel Shor, Carson McNeil, Yotam Intrator, Joseph R. Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg
Publikováno v:
Discover Artificial Intelligence, Vol 4, Iss 1, Pp 1-13 (2024)
Abstract Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for evaluating performance on unseen data require expensive and time-intensive labels. We show that a "Masked Sia
Externí odkaz:
https://doaj.org/article/9dffcaa94a614e3b81d39f5a3c785c1d
Publikováno v:
The Egyptian Journal of Internal Medicine, Vol 36, Iss 1, Pp 1-5 (2024)
Externí odkaz:
https://doaj.org/article/3d48f2227e1546dbbb072886b6c0fc13
Publikováno v:
IEEE Open Journal of Engineering in Medicine and Biology, Vol 6, Pp 147-151 (2025)
Owing to the rapid progress in artificial intelligence (AI) and the widespread use of generative learning, the problem of sparse data has been solved effectively in various research fields. The application of AI technologies has resulted in important
Externí odkaz:
https://doaj.org/article/acbd70ac7a224ec6a08fc9ce0bbe555d
Autor:
Francesca Patterson, Melina A. Kunar
Publikováno v:
Cognitive Research, Vol 9, Iss 1, Pp 1-13 (2024)
Abstract Computer Aided Detection (CAD) has been used to help readers find cancers in mammograms. Although these automated systems have been shown to help cancer detection when accurate, the presence of CAD also leads to an over-reliance effect where
Externí odkaz:
https://doaj.org/article/a6a1673688fa4f248b118459b02073ac
Autor:
Alok Nath, Zia Hashim, Saumya Shukla, Prasanth Areekkara Poduvattil, Zafar Neyaz, Richa Mishra, Manika Singh, Nikhil Misra, Ankit Shukla
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Tuberculosis (TB) is the leading cause of mortality among infectious diseases globally. Effectively managing TB requires early identification of individuals with TB disease. Resource-constrained settings often lack skilled professionals for
Externí odkaz:
https://doaj.org/article/e8fe4213e66c4de58b048ce5c1026c79
Autor:
Eui Jin Hwang
Publikováno v:
Journal of the Korean Society of Radiology, Vol 85, Iss 4, Pp 693-704 (2024)
Artificial intelligence (AI) technology is actively being applied for the interpretation of medical imaging, such as chest X-rays. AI-based software medical devices, which automatically detect various types of abnormal findings in chest X-ray image
Externí odkaz:
https://doaj.org/article/7e32320b6da8496b9a00a3710fd471ee
Autor:
Junghoon Kim
Publikováno v:
Journal of the Korean Society of Radiology, Vol 85, Iss 4, Pp 705-713 (2024)
This article discusses studies and real-world experiences related to the clinical application of artificial intelligence-based computer-aided detection (AI-CAD) software (LuCAS-plus, Monitor Corporation) in detecting pulmonary nodules. During clini
Externí odkaz:
https://doaj.org/article/26c189cc4c1e4b8ab3390b95add3e791
Publikováno v:
JGH Open, Vol 8, Iss 9, Pp n/a-n/a (2024)
Abstract Background and Aims The utilization of artificial intelligence (AI) with computer‐aided detection (CADe) has the potential to increase the adenoma detection rate (ADR) by up to 30% in expert settings and specialized centers. The impact of
Externí odkaz:
https://doaj.org/article/65368cbfe7a44bc4a747be1eadd4a1c0
Autor:
Saad Ali Amin, Mashal Kasem Sulieman Alqudah, Saleh Ateeq Almutairi, Rasha Almajed, Mohammad Rustom Al Nasar, Hamzah Ali Alkhazaleh
Publikováno v:
Heliyon, Vol 10, Iss 14, Pp e34050- (2024)
This study proposes a hierarchical automated methodology for detecting brain tumors in Magnetic Resonance Imaging (MRI), focusing on preprocessing images to improve quality and eliminate artifacts or noise. A modified Extreme Learning Machine is then
Externí odkaz:
https://doaj.org/article/0962bd2cb53d48cd917c5daa24112af8
Autor:
Chia-Ying Lin, Shu-Mei Guo, Jenn-Jier James Lien, Tzung-Yi Tsai, Yi-Sheng Liu, Chao-Han Lai, I-Lin Hsu, Chao-Chun Chang, Yau-Lin Tseng
Publikováno v:
Cancer Imaging, Vol 24, Iss 1, Pp 1-9 (2024)
Abstract Background Low-dose computed tomography (LDCT) has been shown useful in early lung cancer detection. This study aimed to develop a novel deep learning model for detecting pulmonary nodules on chest LDCT images. Methods In this secondary anal
Externí odkaz:
https://doaj.org/article/d625fe2313ed4312ac46ae97d10099f1