Zobrazeno 1 - 10
of 270
pro vyhledávání: '"Pu, Jiantao"'
Abstract Background: Pulmonary function tests (PFTs) and computed tomography (CT) imaging are vital in diagnosing, managing, and monitoring lung diseases. A common issue in practice is the lack of access to recorded pulmonary functions despite availa
Externí odkaz:
http://arxiv.org/abs/2408.05645
Autor:
Ren, Shangsi, Beeche, Cameron A., Shi, Zhiyi, Garcia, Maria Acevedo, Zychowski, Katherine, Leng, Shuguang, Roghanchi, Pedram, Pu, Jiantao
This study aims to establish the causal relationship network between various factors leading to workday loss in underground coal mines using a novel causal artificial intelligence (AI) method. The analysis utilizes data obtained from the National Ins
Externí odkaz:
http://arxiv.org/abs/2402.05940
Autor:
Zhang, Jingping, He, Liyu, Han, Tingting, Tong, Jiayin, Ren, Jialiang, Pu, Jiantao, Zhang, Ming, Guo, Youmin, Jin, Chenwang
Publikováno v:
In Heliyon 15 June 2024 10(11)
Autor:
Pu, Jiantao, Gezer, Naciye Sinem, Ren, Shangsi, Alpaydin, Aylin Ozgen, Avci, Emre Ruhat, Risbano, Michael G., Rivera-Lebron, Belinda, Chan, Stephen Yu-Wah, Leader, Joseph K.
Publikováno v:
In Medical Image Analysis October 2023 89
Publikováno v:
In Safety Science August 2023 164
To investigate whether and to what extent central serous chorioretinopathy (CSC) depicted on color fundus photographs can be assessed using deep learning technology. We collected a total of 2,504 fundus images acquired on different subjects. We verif
Externí odkaz:
http://arxiv.org/abs/1901.04540
Autor:
Gezer, Naciye S., Bandos, Andriy I., Beeche, Cameron A., Leader, Joseph K., Dhupar, Rajeev, Pu, Jiantao
Publikováno v:
In Lung Cancer May 2023 179
Objective: To validate and compare the performance of eight available deep learning architectures in grading the severity of glaucoma based on color fundus images. Materials and Methods: We retrospectively collected a dataset of 5978 fundus images an
Externí odkaz:
http://arxiv.org/abs/1810.13376
Publikováno v:
Comput Med Imaging Graph, 2019
This study aims to automatically diagnose thoracic diseases depicted on the chest x-ray (CXR) images using deep convolutional neural networks. The existing methods generally used the entire CXR images for training purposes, but this strategy may suff
Externí odkaz:
http://arxiv.org/abs/1810.12959
Autor:
Beeche, Cameron, Singh, Jatin P, Leader, Joseph K, Gezer, Naciye S, Oruwari, Amechi P, Dansingani, Kunal K, Chhablani, Jay, Pu, Jiantao
Publikováno v:
In Pattern Recognition August 2022 128