Zobrazeno 1 - 10
of 90
pro vyhledávání: '"Jang-Hwan Choi"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023)
Abstract Although diabetes mellitus is a complex and pervasive disease, most studies to date have focused on individual features, rather than considering the complexities of multivariate, multi-instance, and time-series data. In this study, we develo
Externí odkaz:
https://doaj.org/article/6dc4d291920b4c13ad1d3add45f22da6
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-10 (2023)
Abstract In the medical field, various clinical information has been accumulated to help clinicians provide personalized medicine and make better diagnoses. As chronic diseases share similar characteristics, it is possible to predict multiple chronic
Externí odkaz:
https://doaj.org/article/48aaca0f1a654631af1ea7421299a6ba
Publikováno v:
BioMedical Engineering OnLine, Vol 21, Iss 1, Pp 1-22 (2022)
Abstract Background To obtain phase-contrast X-ray images, single-grid imaging systems are effective, but Moire artifacts remain a significant issue. The solution for removing Moire artifacts from an image is grid rotation, which can distinguish betw
Externí odkaz:
https://doaj.org/article/c0bfcb60fdb14e85812bb915400aeae5
Publikováno v:
PeerJ Computer Science, Vol 9, p e1311 (2023)
Predicting recurrence in patients with non-small cell lung cancer (NSCLC) before treatment is vital for guiding personalized medicine. Deep learning techniques have revolutionized the application of cancer informatics, including lung cancer time-to-e
Externí odkaz:
https://doaj.org/article/778fe78bdb644ed8aa9dd20fdc4f1e43
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022)
Abstract Several state-of-the-art object detectors have demonstrated outstanding performances by optimizing feature representation through modification of the backbone architecture and exploitation of a feature pyramid. To determine the effectiveness
Externí odkaz:
https://doaj.org/article/f48228e18836406d8672a3b0f3b3c00c
Publikováno v:
IEEE Access, Vol 10, Pp 126580-126592 (2022)
Deep neural networks have shown great improvements in low-dose computed tomography (CT) denoising. Early deep learning-based low-dose CT denoising algorithms were primarily based on supervised learning. However, supervised learning requires a large n
Externí odkaz:
https://doaj.org/article/d4e5f49d39894ae599eb404558ff7e2b
Publikováno v:
IEEE Access, Vol 9, Pp 71821-71831 (2021)
Detector saturation in cone-beam computed tomography occurs when an object of highly varying shape and material composition is imaged using an automatic exposure control (AEC) system. When imaging a subject’s knees, high beam energy ensures the vis
Externí odkaz:
https://doaj.org/article/64456542b8e44de4b1eb195324511b47
Publikováno v:
PLoS ONE, Vol 17, Iss 9, p e0274308 (2022)
Deep neural networks have shown great improvements in low-dose computed tomography (CT) denoising. Early algorithms were primarily optimized to obtain an accurate image with low distortion between the denoised image and reference full-dose image at t
Externí odkaz:
https://doaj.org/article/a6928a5fd7884e48892cd834ad5dc310
Publikováno v:
PLoS ONE, Vol 17, Iss 9, p e0272961 (2022)
Deep convolutional networks have been developed to detect prohibited items for automated inspection of X-ray screening systems in the transport security system. To our knowledge, the existing frameworks were developed to recognize threats using only
Externí odkaz:
https://doaj.org/article/b15735635875438ba19d600975d7fc88
Publikováno v:
Sensors, Vol 22, Iss 17, p 6594 (2022)
Due to high recurrence rates in patients with non-small cell lung cancer (NSCLC), medical professionals need extremely accurate diagnostic methods to prevent bleak prognoses. However, even the most commonly used diagnostic method, the TNM staging sys
Externí odkaz:
https://doaj.org/article/69068836587e4d6786f744f2e52c36e6