AIMC Topic: Radiography
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A comprehensive deep-learning model for interpreting chest x-rays.
The Lancet. Digital health
Jan 1, 2022
Distinguishing Intramedullary Spinal Cord Neoplasms from Non-Neoplastic Conditions by Analyzing the Classic Signs on MRI in the Era of AI.
Current medical imaging
Jan 1, 2022
Intramedullary lesions can be challenging to diagnose, given the wide range of possible pathologies. Each lesion has unique clinical and imaging features, which are best evaluated using magnetic resonance imaging. Radiological imaging is unique with ...
Evidence-Based Artificial Intelligence in Medical Imaging.
PET clinics
Jan 1, 2022
Artificial intelligence (AI) in medical imaging is in its infancy. However, ongoing advances in hardware and software as well as increasing access to ever-expanding datasets for training, validation, and testing purposes are likely to make AI an incr...
Application of Artificial Intelligence in PET Instrumentation.
PET clinics
Jan 1, 2022
Artificial intelligence (AI) has been widely used throughout medical imaging, including PET, for data correction, image reconstruction, and image processing tasks. However, there are number of opportunities for the application of AI in photon detecto...
Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities.
PET clinics
Jan 1, 2022
Almost 1 in 10 individuals can suffer from one of many rare diseases (RDs). The average time to diagnosis for an RD patient is as high as 7 years. Artificial intelligence (AI)-based positron emission tomography (PET), if implemented appropriately, ha...
Effective End-to-End Deep Learning Process in Medical Imaging Using Independent Task Learning: Application for Diagnosis of Maxillary Sinusitis.
Yonsei medical journal
Dec 1, 2021
PURPOSE: This study aimed to propose an effective end-to-end process in medical imaging using an independent task learning (ITL) algorithm and to evaluate its performance in maxillary sinusitis applications.
Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks.
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
Lung nodules are commonly missed in chest radiographs. We propose and evaluate P-AnoGAN, an unsupervised anomaly detection approach for lung nodules in radiographs. P-AnoGAN modifies the fast anomaly detection generative adversarial network (f-AnoGAN...
Anatomical Landmark Detection using Deep Appearance-Context Network.
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
Accurate identification of anatomical landmarks is a crucial step in medical image analysis. While deep neural networks have shown impressive performance on computer vision tasks, they rely on a large amount of data, which is often not available. In ...
Deep Learning Framework for Automatic Bone Age Assessment.
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
Bone age Assessment or the skeletal age is a general clinical practice to detect endocrine and metabolic disarrangement in child development. The bone age indicates the level of structural and biological growth better than chronological age calculate...
Lung contour detection in Chest X-ray images using Mask Region-based Convolutional Neural Network and Adaptive Closed Polyline Searching Method.
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
Detection of lung contour on chest X-ray images (CXRs) is a necessary step for computer-aid medical imaging analysis. Because of the low-intensity contrast around lung boundary and large inter-subject variance, it is challenging to detect lung from s...
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