Zobrazeno 1 - 10
of 299
pro vyhledávání: '"computer assisted detection"'
Publikováno v:
European Journal of Radiology Open, Vol 11, Iss , Pp 100509- (2023)
Purpose: To evaluate the stand-alone diagnostic performances of AI-CAD and outcomes of AI-CAD detected abnormalities when applied to the mammographic interpretation workflow. Methods: From January 2016 to December 2017, 6499 screening mammograms of 5
Externí odkaz:
https://doaj.org/article/334e8b4c51d9486ba9d80f1d7d6d6cfb
Publikováno v:
Breast, Vol 65, Iss , Pp 124-135 (2022)
Purpose: The purpose of this study was to compare the diagnostic performance and the interpretation time of breast ultrasound examination between reading without and with the artificial intelligence (AI) system as a concurrent reading aid. Material a
Externí odkaz:
https://doaj.org/article/8703691548bc4fc1a784918ed9a00fd7
Publikováno v:
Mathematical Biosciences and Engineering, Vol 19, Iss 10, Pp 10037-10059 (2022)
Obtaining massive amounts of training data is often crucial for computer-assisted diagnosis using deep learning. Unfortunately, patient data is often small due to varied constraints. We develop a new approach to extract significant features from a sm
Externí odkaz:
https://doaj.org/article/e9ebb2d3a1954104b7c8de4ac6b0a460
Akademický článek
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Akademický článek
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Autor:
Hansmann Martin-Leo, Klauschen Frederick, Samek Wojciech, Müller Klaus-Robert, Donnadieu Emmanuel, Scharf Sonja, Hartmann Sylvia, Koch Ina, Ackermann Jörg, Pantanowitz Liron, Schäfer Hendrik, Wurzel Patrick
Publikováno v:
Journal of Pathology Informatics, Vol 14, Iss , Pp 100298- (2023)
In recent years, medical disciplines have moved closer together and rigid borders have been increasingly dissolved. The synergetic advantage of combining multiple disciplines is particularly important for radiology, nuclear medicine, and pathology to
Externí odkaz:
https://doaj.org/article/ac8621038fe94564b564579214dd5f00
Autor:
Daiju Ueda, Akira Yamamoto, Akitoshi Shimazaki, Shannon Leigh Walston, Toshimasa Matsumoto, Nobuhiro Izumi, Takuma Tsukioka, Hiroaki Komatsu, Hidetoshi Inoue, Daijiro Kabata, Noritoshi Nishiyama, Yukio Miki
Publikováno v:
BMC Cancer, Vol 21, Iss 1, Pp 1-8 (2021)
Abstract Background We investigated the performance improvement of physicians with varying levels of chest radiology experience when using a commercially available artificial intelligence (AI)-based computer-assisted detection (CAD) software to detec
Externí odkaz:
https://doaj.org/article/d337bd33bc62492a8fcdb776a3cf9948
Autor:
Youngjune Kim, Jiwon Rim, Sun Mi Kim, Bo La Yun, So Yeon Park, Hye Shin Ahn, Bohyoung Kim, Mijung Jang
Publikováno v:
Ultrasonography, Vol 40, Iss 1, Pp 83-92 (2021)
Purpose The purpose of this study was to measure the cancer detection rate of computer-aided detection (CAD) software in preoperative automated breast ultrasonography (ABUS) of breast cancer patients and to determine the characteristics associated wi
Externí odkaz:
https://doaj.org/article/37caff91b16947e09a49adab8c155078
Autor:
Heng-Sheng Chao, Chiao-Yun Tsai, Chung-Wei Chou, Tsu-Hui Shiao, Hsu-Chih Huang, Kun-Chieh Chen, Hao-Hung Tsai, Chin-Yu Lin, Yuh-Min Chen
Publikováno v:
Biomedicines, Vol 11, Iss 1, p 147 (2023)
Low-dose computed tomography (LDCT) has emerged as a standard method for detecting early-stage lung cancer. However, the tedious computer tomography (CT) slide reading, patient-by-patient check, and lack of standard criteria to determine the vague bu
Externí odkaz:
https://doaj.org/article/5c994ce12929431f92a83bd35b04af59
Publikováno v:
BioMedical Engineering OnLine, Vol 19, Iss 1, Pp 1-10 (2020)
Abstract Background As the rupture of cerebral aneurysm may lead to fatal results, early detection of unruptured aneurysms may save lives. At present, the contrast-unenhanced time-of-flight magnetic resonance angiography is one of the most commonly u
Externí odkaz:
https://doaj.org/article/a48bb545257c4a2fa8ed16a2b002144e