AIMC Topic: Diagnostic Imaging
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[Application of Artificial Intelligence in Clinical Genomics].
Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae
Dec 30, 2021
Clinical genomics mainly studies the clinical application of genomics in diagnosis,treatment decision,and prognosis prediction.Artificial intelligence enables the processing of complex and massive data in genomics which are difficult to be dealt with...
Radiomics, deep learning and early diagnosis in oncology.
Emerging topics in life sciences
Dec 21, 2021
Medical imaging, including X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), plays a critical role in early detection, diagnosis, and treatment response prediction of cancer. To ease radiologists' task and help with challenging c...
A Learning Framework for Medical Image-Based Intelligent Diagnosis from Imbalanced Datasets.
Studies in health technology and informatics
Nov 18, 2021
Medical image classification and diagnosis based on machine learning has made significant achievements and gradually penetrated the healthcare industry. However, medical data characteristics such as relatively small datasets for rare diseases or imba...
Automatic segmentation of paravertebral muscles in abdominal CT scan by U-Net: The application of data augmentation technique to increase the Jaccard ratio of deep learning.
Medicine
Nov 5, 2021
Sarcopenia, characterized by a decline of skeletal muscle mass, has emerged as an important prognostic factor for cancer patients. Trunk computed tomography (CT) is a commonly used modality for assessment of cancer disease extent and treatment outcom...
Towards Data Integration for AI in Cancer Research.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Nov 1, 2021
Cancer research is increasing relying on data-driven methods and Artificial Intelligence (AI), to increase accuracy and efficiency in decision making. Such methods can solve a variety of clinically relevant problems in cancer diagnosis and treatment,...
The false hope of current approaches to explainable artificial intelligence in health care.
The Lancet. Digital health
Nov 1, 2021
The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has been argued that explainable AI will engender trust with the health-c...
Preface: Artificial Intelligence in Radiology: Opportunities and Challenges.
Radiologic clinics of North America
Nov 1, 2021
Upstream Machine Learning in Radiology.
Radiologic clinics of North America
Nov 1, 2021
Machine learning (ML) and Artificial intelligence (AI) has the potential to dramatically improve radiology practice at multiple stages of the imaging pipeline. Most of the attention has been garnered by applications focused on improving the end of th...
Optimization of Radiology Workflow with Artificial Intelligence.
Radiologic clinics of North America
Nov 1, 2021
The potential of artificial intelligence (AI) in radiology goes far beyond image analysis. AI can be used to optimize all steps of the radiology workflow by supporting a variety of nondiagnostic tasks, including order entry support, patient schedulin...
Basic Artificial Intelligence Techniques: Machine Learning and Deep Learning.
Radiologic clinics of North America
Nov 1, 2021
Machine learning is an important tool for extracting information from medical images. Deep learning has made this more efficient by not requiring an explicit feature extraction step and in some cases detecting features that humans had not identified....
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