Ads
related to: medical imaging using deep learning
Search results
Results From The WOW.Com Content Network
Medical open network for AI (MONAI) is an open-source, community-supported framework for Deep learning (DL) in healthcare imaging. MONAI provides a collection of domain-optimized implementations of various DL algorithms and utilities specifically designed for medical imaging tasks.
Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine. This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care.
Medical imaging (such as X-ray and photography) is a commonly used tool in dermatology [55] and the development of deep learning has been strongly tied to image processing. Therefore, there is a natural fit between the dermatology and deep learning. Machine learning learning holds great potential to process these images for better diagnoses. [56]
Examples include the pyOsirix [4] scripting tool for the popular Osirix application, the pyradiomics python package for extracting radiomic features from medical imaging, [5] the 3DSlicer image analysis application, the SimpleElastix medical image registration library, [6] and the NiftyNet deep learning library for medical imaging. [7]
A February 2016 paper from his lab exploring convolutional neural network architectures and transfer learning for lymph node detection and interstitial lung disease classification had over 1,000 citations as of early 2019. [8] In 2018 he was the keynote speaker at the inaugural Medical Imaging and Deep Learning (MIDL) conference. [9]
This process as a whole significantly accelerates the MRI process. Image segmentation or identification of lesions can be achieved through machine learning. In deep learning, with a convolutional neural network, the mapping function can be specified by the network. ML and DL improve image resolution as well as imaging speed. [37]