PulmoNet: a novel deep learning based pulmonary diseases detection model
BMC Medical Imaging
by AbdulRahman Tosho Abdulahi, Roseline Oluwaseun Ogundokun, Ajiboye Raimot Adenike, Mohd Asif Shah and Yusuf Kola Ahmed
8h ago
Pulmonary diseases are various pathological conditions that affect respiratory tissues and organs, making the exchange of gas challenging for animals inhaling and exhaling. It varies from gentle and self-limit ..read more
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Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images
BMC Medical Imaging
by Minyue Yin, Chao Xu, Jinzhou Zhu, Yuhan Xue, Yijia Zhou, Yu He, Jiaxi Lin, Lu Liu, Jingwen Gao, Xiaolin Liu, Dan Shen and Cuiping Fu
8h ago
Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML ..read more
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Influence of spectral shaping and tube voltage modulation in ultralow-dose computed tomography of the abdomen
BMC Medical Imaging
by Philipp Feldle, Jan-Peter Grunz, Andreas Steven Kunz, Pauline Pannenbecker, Theresa Sophie Patzer, Svenja Pichlmeier, Stephanie Tina Sauer, Robin Hendel, Süleyman Ergün, Thorsten Alexander Bley and Henner Huflage
4d ago
Unenhanced abdominal CT constitutes the diagnostic standard of care in suspected urolithiasis. Aiming to identify potential for radiation dose reduction in this frequent imaging task, this experimental study c ..read more
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MTFN: multi-temporal feature fusing network with co-attention for DCE-MRI synthesis
BMC Medical Imaging
by Wei Li, Jiaye Liu, Shanshan Wang and Chaolu Feng
1w ago
Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) plays an important role in the diagnosis and treatment of breast cancer. However, obtaining complete eight temporal images of DCE-MRI requires a l ..read more
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Clinical and multiparametric MRI features for differentiating uterine carcinosarcoma from endometrioid adenocarcinoma
BMC Medical Imaging
by Xiaodan Chen, Qingyong Guo, Xiaorong Chen, Wanjing Zheng, Yaqing Kang and Dairong Cao
1w ago
The purpose of our study was to differentiate uterine carcinosarcoma (UCS) from endometrioid adenocarcinoma (EAC) by the multiparametric magnetic resonance imaging (MRI) features ..read more
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How to identify juxtaglomerular cell tumor by ultrasound: a case series and review of the literature
BMC Medical Imaging
by Li Wang, Meiying Li, Siqi Jin, Yunshu Ouyang, Fenglan Wang, Ke Lv, Jianchu Li, Yuxin Jiang, He Liu and Qingli Zhu
1w ago
To study the value of ultrasound in the diagnosis of juxtaglomerular cell tumor (JGCT ..read more
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CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer
BMC Medical Imaging
by Jiexiao Wang, Jialiang Wang, Xiang Huang, Yanfei Zhou, Jian Qi, Xiaojun Sun, Jinfu Nie, Zongtao Hu, Shujie Wang, Bo Hong and Hongzhi Wang
2w ago
Tumor mutational burden (TMB) is one of the most significant predictive biomarkers of immunotherapy efficacy in non-small cell lung cancer (NSCLC). Radiomics allows high-throughput extraction and analysis of ..read more
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Development of a multi-phase CT-based radiomics model to differentiate heterotopic pancreas from gastrointestinal stromal tumor
BMC Medical Imaging
by Kui Sun, Shuxia Yu, Ying Wang, Rongze Jia, Rongchao Shi, Changhu Liang, Ximing Wang and Haiyan Wang
2w ago
To investigate whether CT-based radiomics can effectively differentiate between heterotopic pancreas (HP) and gastrointestinal stromal tumor (GIST), and whether different resampling methods can affect the mode ..read more
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Ultrasound characteristics of normal parathyroid glands and analysis of the factors affecting their display
BMC Medical Imaging
by Cuiping Wu, Binyang Zhu, Song Kang, Shiyu Wang, Yingying Liu, Xue Mei, He Zhang and Shuangquan Jiang
2w ago
Parathyroid glands are important endocrine glands, and the identification of normal parathyroid glands is crucial for their protection. The aim of this study is to explore the sonographic characteristics of no ..read more
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Automated assessment of cardiac pathologies on cardiac MRI using T1-mapping and late gadolinium phase sensitive inversion recovery sequences with deep learning
BMC Medical Imaging
by Aleksandra M. Paciorek, Claudio E. von Schacky, Sarah C. Foreman, Felix G. Gassert, Florian T. Gassert, Jan S. Kirschke, Karl-Ludwig Laugwitz, Tobias Geith, Martin Hadamitzky and Jonathan Nadjiri
2w ago
A deep learning (DL) model that automatically detects cardiac pathologies on cardiac MRI may help streamline the diagnostic workflow. To develop a DL model to detect cardiac pathologies on cardiac MRI T1-mappi ..read more
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