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Mashup Score: 1Radiology: Artificial Intelligence - 2 month(s) ago
Can’t sign in? Forgot your password? If the address matches an existing account you will receive an email with instructions to reset your password. Can’t sign in? Forgot your username? An end-to-end deep learning pipeline provides standardized segmentation for patients with single ventricle physiology (Yao and St. Clair et al). A CNN trained on data from three brain cancer datasets accurately classifies MR image sequences (Mahmutoglu et al). See the latest information about Radiology: Artificial Intelligen
Source: pubs.rsna.orgCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 5Celebrating the Journal’s First 5 Years - 3 month(s) ago
Can’t sign in? Forgot your password? If the address matches an existing account you will receive an email with instructions to reset your password. Can’t sign in? Forgot your username? 1. Kahn CE Jr.. Radiol Artif Intell 2023;5(6):e230418. Link, Google Scholar 2. Brady AP, Allen B, Chong J, et al.. Radiol Artif Intell 2024;6(1):e230513. Link, Google Scholar 3. Hwang EJ, Jeong WG, David PM, Arentz M, Ruhwald M, Yoon SH.. Radiol Artif Intell 2024:6(2):e230327. Link, Google
Source: pubs.rsna.orgCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 1AI for Detection of Tuberculosis: Implications for Global Health - 3 month(s) ago
Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiography has expanded in tuberculosis triage and screening beyond its traditional complementary role in the diagnosis of tuberculosis. Computer-aided diagnosis (CAD) systems for tuberculosis detection on chest radiographs have recently made substantial progress in diagnostic performance, thanks to deep learning technologies. The current performance of CAD systems for tuberculosis has approximated that of human experts, presenting a potential solution to the shortage of human readers to interpret chest radiographs in low- or middle-income, high-tuberculosis-burden countries. This article provides a critical appraisal of developmental process reporting in extant CAD software for tuberculosis, based on the Checklist for Artificial Intelligence in Medical Imaging. It also explores several considerations to scale up CAD solutions, encompassing manufa
Source: pubs.rsna.orgCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 3Radiology: Artificial Intelligence - 3 month(s) ago
Can’t sign in? Forgot your password? If the address matches an existing account you will receive an email with instructions to reset your password. Can’t sign in? Forgot your username? by Charles E. Kahn, Jr, MD, MS Editor, Radiology: Artificial Intelligence I am pleased to announce that RSNA and four other leading radiology societies published today a joint statement on the development and use of artificial intelligence (AI) tools in radiology. This statement provides important guidance for our profession
Source: pubs.rsna.orgCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 5Celebrating the Journal’s First 5 Years - 3 month(s) ago
Can’t sign in? Forgot your password? If the address matches an existing account you will receive an email with instructions to reset your password. Can’t sign in? Forgot your username? 1. Kahn CE Jr.. Radiol Artif Intell 2023;5(6):e230418. Link, Google Scholar 2. Brady AP, Allen B, Chong J, et al.. Radiol Artif Intell 2024;6(1):e230513. Link, Google Scholar 3. Hwang EJ, Jeong WG, David PM, Arentz M, Ruhwald M, Yoon SH.. Radiol Artif Intell 2024:6(2):e230327. Link, Google
Source: pubs.rsna.orgCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 1AI in radiology: helper or bane of society and the environment? - 3 month(s) ago
The climate crisis and AI – arguably two of the most hotly-debated and relevant topics of our time – share an intricate relationship: While computation of complex AI routines commands an immense carbon footprint, it is these algorithms that might be the very key to mitigate the effects of global warming. In a dedicated session at ECR 2023, radiologists explored the societal and environmental impact of the technology in healthcare.
Source: healthcare-in-europe.comCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 9Examination-Level Supervision for Deep Learning–based Intracranial Hemorrhage Detection on Head CT Scans - 3 month(s) ago
Purpose To compare the effectiveness of weak supervision (ie, with examination-level labels only) and strong supervision (ie, with image-level labels) in training deep learning models for detection of intracranial hemorrhage (ICH) on head CT scans. Materials and Methods In this retrospective study, an attention-based convolutional neural network was trained with either local (ie, image level) or global (ie, examination level) binary labels on the Radiological Society of North America (RSNA) 2019 Brain CT Hemorrhage Challenge dataset of 21 736 examinations (8876 [40.8%] ICH) and 752 422 images (107 784 [14.3%] ICH). The CQ500 (436 examinations; 212 [48.6%] ICH) and CT-ICH (75 examinations; 36 [48.0%] ICH) datasets were employed for external testing. Performance in detecting ICH was compared between weak (examination-level labels) and strong (image-level labels) learners as a function of the number of labels available during training. Results On examination-level binary classification, s
Source: pubs.rsna.orgCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 11Automated detection of causal relationships among diseases and imaging findings in textual radiology reports - 4 month(s) ago
AbstractObjective. Textual radiology reports contain a wealth of information that may help understand associations among diseases and imaging observations. This
Source: academic.oup.comCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 3Radiology: Artificial Intelligence - 4 month(s) ago
Can’t sign in? Forgot your password? If the address matches an existing account you will receive an email with instructions to reset your password. Can’t sign in? Forgot your username? by Charles E. Kahn, Jr, MD, MS Editor, Radiology: Artificial Intelligence I am pleased to announce that RSNA and four other leading radiology societies published today a joint statement on the development and use of artificial intelligence (AI) tools in radiology. This statement provides important guidance for our profession
Source: pubs.rsna.orgCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 1Radiology: Artificial Intelligence - 4 month(s) ago
Can’t sign in? Forgot your password? If the address matches an existing account you will receive an email with instructions to reset your password. Can’t sign in? Forgot your username? An end-to-end deep learning pipeline provides standardized segmentation for patients with single ventricle physiology (Yao and St. Clair et al). A CNN trained on data from three brain cancer datasets accurately classifies MR image sequences (Mahmutoglu et al). See the latest information about Radiology: Artificial Intelligen
Source: pubs.rsna.orgCategories: General Medicine News, Future of MedicineTweet
RT @Radiology_AI: Radiology: Artificial Intelligence offers leading-edge #MachineLearning research ➡️ https://t.co/RFrCO88d7j @wacv_offic…