-
Mashup Score: 2
Supplemental material is available for this article. Keywords: Informatics, MR Diffusion Tensor Imaging, MR Perfusion, MR Imaging, Neuro-Oncology, CNS, Brain/Brain Stem, Oncology, Radiogenomics, Radiology-Pathology Integration © RSNA, 2022
Categories: Latest Headlines, RadiologyTweet
-
Mashup Score: 3Exploring the Acceleration Limits of Deep Learning Variational Network–based Two-dimensional Brain MRI - 1 year(s) ago
Purpose To explore the limits of deep learning–based brain MRI reconstruction and identify useful acceleration ranges for general-purpose imaging and potential screening. Materials and Methods In this retrospective study conducted from 2019 through 2021, a model was trained for reconstruction on 5847 brain MR images. Performance was evaluated across a wide range of accelerations (up to 100-fold…
Categories: Latest Headlines, RadiologyTweet-
RT @Radiology_AI: Exploring the Acceleration Limits of Deep Learning VarNet-based 2-D Brain MRI https://t.co/suLntUhgZY @NYUImaging @aibe_f…
-
-
Mashup Score: 1Artificial Intelligence and Radiology Education - 1 year(s) ago
Implementation of artificial intelligence (AI) applications into clinical practice requires AI-savvy radiologists to ensure the safe, ethical, and effective use of these systems for patient care. Increasing demand for AI education reflects recognition of the translation of AI applications from research to clinical practice, with positive trainee attitudes regarding the influence of AI on…
Categories: Latest Headlines, RadiologyTweet-
RT @Radiology_AI: @AliTejaniMD & coauthors identify resources for formal and self-directed learning in #AI https://t.co/OrkNpXbUb8 @cekahn…
-
-
Mashup Score: 6
“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Despite frequent reports of…
Categories: Latest Headlines, RadiologyTweet-
RT @Radiology_AI: Underreported noise in schema design and annotations limits deployment of #MachineLearning models https://t.co/TasMYsiJTw…
-
-
Mashup Score: 3Radiology: Artificial Intelligence - 1 year(s) ago
NOVOCTSEPJUNAPRMARFEBJAN Posted 11/7/2022Journal Vision: Bias in Machine Learning by Ali Tejani, MD As artificial intelligence (AI) solutions translate to clinical practice, limitations of imaging AI become apparent, deriving in part from bias in medical imaging AI systems. Though bias in AI may originate…
Categories: Latest Headlines, RadiologyTweet-
RT @Radiology_AI: Follow the @Radiology_AI blog -- The Vasty Deep! https://t.co/abJpdqDRZw #ML #MachineLearning #ArtificialIntelligence htt…
-
-
Mashup Score: 5Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls - 1 year(s) ago
Purpose To investigate the impact of the following three methodological pitfalls on model generalizability: (a) violation of the independence assumption, (b) model evaluation with an inappropriate performance indicator or baseline for comparison, and (c) batch effect. Materials and Methods The authors used retrospective CT, histopathologic analysis, and radiography datasets to develop machine…
Categories: Latest Headlines, RadiologyTweet-
RT @Radiology_AI: Investigation of 3 key methodologic pitfalls in #AI model development https://t.co/hKvbBgIG6z @AskFarhad @Ucalgary @UofC_…
-
-
Mashup Score: 4Exploring the Acceleration Limits of Deep Learning Variational Network–based Two-dimensional Brain MRI - 1 year(s) ago
Purpose To explore the limits of deep learning–based brain MRI reconstruction and identify useful acceleration ranges for general-purpose imaging and potential screening. Materials and Methods In this retrospective study conducted from 2019 through 2021, a model was trained for reconstruction on 5847 brain MR images. Performance was evaluated across a wide range of accelerations (up to 100-fold…
Categories: Latest Headlines, RadiologyTweet-
RT @Radiology_AI: End-to-End Variational Network reconstructed 2D brain MRIs over a range of undersampling rates https://t.co/suLntUhgZY @N…
-
-
Mashup Score: 5The EMory BrEast Imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4M Screening and Diagnostic Mammographic Images - 1 year(s) ago
“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. The EMBED dataset contains…
Categories: Latest Headlines, RadiologyTweet-
RT @Radiology_AI: 20% of @EmoryRadiology Breast Imaging Dataset dataset will be freely available for research https://t.co/DGjXZMwv4O @hari…
-
-
Mashup Score: 1Assessing Methods and Tools to Improve Reporting, Increase Transparency, and Reduce Failures in Machine Learning Applications in Health Care - 1 year(s) ago
Artificial intelligence applications for health care have come a long way. Despite the remarkable progress, there are several examples of unfulfilled promises and outright failures. There is still a struggle to translate successful research into successful real-world applications. Machine learning (ML) products diverge from traditional software products in fundamental ways. Particularly, the main…
Categories: Latest Headlines, RadiologyTweet-
#AI in Radiology article alert: Assessing Methods and Tools to Improve Reporting, Increase Transparency, and Reduce Failures in Machine Learning Applications in Health Care (Radiol Artif Intell) https://t.co/bhbplVP3jc @Radiology_AI @RSNA #ML #MachineLearning https://t.co/QCnV1MIvZg
-
-
Mashup Score: 4The EMory BrEast Imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4M Screening and Diagnostic Mammographic Images - 1 year(s) ago
“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. The EMBED dataset contains…
Categories: Future of Medicine, Latest HeadlinesTweet-
RT @Radiology_AI: 20% of @EmoryRadiology Breast Imaging Dataset dataset will be freely available for research https://t.co/DGjXZMwv4O @hari…
-
Journal Watch: The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset (Radiol Artif Intell) https://t.co/gRD3NNWFHZ @Radiology_AI @RSNA #radiology #AI https://t.co/gWWsccltLJ