1,720,997 research outputs found
Use of a novel convolutional neural network-based mammographic evaluation to assess response to adjuvant endocrine therapy in women with early-stage breast cancer.
No full textAbstract P1-02-06: Effect of chemoprevention on convolutional neural network-based breast cancer risk model using a mammographic dataset of women with atypical hyperplasia, lobular and ductal carcinoma in situ
No full textAbstract PR-04: Effect of breast cancer chemoprevention on a convolutional neural network-based mammographic evaluation using a mammographic dataset of women with atypical hyperplasia, lobular or ductal carcinoma in situ
No full textAbstract PS5-21: Use of a novel convolutional neural network (CNN)-based mammographic evaluation to assess response to adjuvant endocrine therapy
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Abstract P2-10-03: Improving breast cancer risk prediction using a convolutional neural network-based mammographic evaluation in combination with clinical risk factors
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Clinical Artificial Intelligence Applications: Musculoskeletal.
No full textWe present an overview of current clinical musculoskeletal imaging applications for artificial intelligence, as well as potential future applications and techniques
Understanding artificial intelligence based radiology studies: What is overfitting?
No full textArtificial intelligence (AI) is a broad umbrella term used to encompass a wide variety of subfields dedicated to creating algorithms to perform tasks that mimic human intelligence. As AI development grows closer to clinical integration, radiologists will need to become familiar with the principles of artificial intelligence to properly evaluate and use this powerful tool. This series aims to explain certain basic concepts of artificial intelligence, and their applications in medical imaging starting with a concept of overfitting
Deciphering musculoskeletal artificial intelligence for clinical applications: how do I get started?
No full textArtificial intelligence (AI) represents a broad category of algorithms for which deep learning is currently the most impactful. When electing to begin the process of building an adequate fundamental knowledge base allowing them to decipher machine learning research and algorithms, clinical musculoskeletal radiologists currently have few options to turn to. In this article, we provide an introduction to the vital terminology to understand, how to make sense of data splits and regularization, an introduction to the statistical analyses used in AI research, a primer on what deep learning can or cannot do, and a brief overview of clinical integration methods. Our goal is to improve the readers\u27 understanding of this field
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