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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: 1Radiology: 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? 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: 1Radiology: 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? 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: 12Tracking hand movements with a smart glove - 3 month(s) ago
Dynamic hand movements could be detected in real time using machine learning and a smart textile glove.
Source: www.science.orgCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 0PROS AND CONS OF MACHINE LEARNING - 3 month(s) ago
The advantages and drawbacks of machine learning in medicine and ophthalmology
Source: ophthalmologymanagement.comCategories: General Medicine News, OphthalmologyTweet-
The following article aims to shed light on the advantages+drawbacks of harnessing Machine learning in the medical+ophthalmological domains, offering a nuanced perspective on its impact+the reasons to engage with this topic https://t.co/O9QRgkDo9e #machinelearning @AndrzejGrzybow https://t.co/cznN0IUDwz
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Mashup Score: 23Machine learning promises to accelerate metabolism research - 3 month(s) ago
A new study shows that it is possible to use machine learning and statistics to address a problem that has long hindered the field of metabolomics: large variations in the data collected at different …
Source: medicalxpress.comCategories: General Medicine News, General NewsTweet
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Mashup Score: 227
Machine learning advances research into early language acquisition in children.
Source: www.science.orgCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 20Using machine learning to battle COVID-19 bacterial co-infection - 3 month(s) ago
University of Queensland researchers have used machine learning to help predict the risk of secondary bacterial infections in hospitalized COVID-19 patients. The research is published in The Lancet Microbe.
Source: medicalxpress.comCategories: General Medicine News, General HCPsTweet
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Mashup Score: 13What’s the Difference Between Machine Learning and TinyML? - 4 month(s) ago
Machine learning, or ML, gave us TinyML. What are the differences between the two, and what makes them unique?
Source: www.electronicdesign.comCategories: General Medicine News, General HCPsTweet
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Mashup Score: 4Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review - 4 month(s) ago
Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models is scarce. We aimed to review health-care professional (HCP) and patient perceptions of ML risk prediction models in published literature, to inform future risk prediction model development. Following database and citation searches, we identified 41 articles suitable for inclusion. Article quality varied with qualitative studies performing strongest.
Source: www.thelancet.comCategories: General Medicine News, Future of MedicineTweet
RT @Radiology_AI: Radiology: Artificial Intelligence offers leading-edge #MachineLearning research ➡️ https://t.co/RFrCO88d7j @wacv_offic…