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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.
The first application of deep learning in PACT was by Reiter et al. [8] in which a deep neural network was trained to learn spatial impulse responses and locate photoacoustic point sources. The resulting mean axial and lateral point location errors on 2,412 of their randomly selected test images were 0.28 mm and 0.37 mm respectively.
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]
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]
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.
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