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Medical Image Computing (the "MIC" in MICCAI) is the field of study involving the application of image processing and computer vision to medical imaging.The goals of medical image computing tasks are diverse, but some common examples are computer-aided diagnosis, image segmentation of anatomical structures and/or abnormalities, and the registration or "alignment" of medical images acquired ...
Research in Computational Molecular Biology (RECOMB) is an annual academic conference on the subjects of bioinformatics and computational biology.The conference has been held every year since 1997 and is widely considered as one of two best international conferences in computational biology publishing rigorously peer-reviewed papers, alongside the ISMB conference.
Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine.
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Medical Image Analysis (MedIA) is a peer-reviewed academic journal which focuses on medical and biological image analysis.The journal publishes papers which contribute to the basic science of analyzing and processing biomedical images acquired through means such as magnetic resonance imaging, ultrasound, computed tomography, nuclear medicine, x-ray, optical and confocal microscopy, among others.
U-Net was created by Olaf Ronneberger, Philipp Fischer, Thomas Brox in 2015 and reported in the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation". [1] It is an improvement and development of FCN: Evan Shelhamer, Jonathan Long, Trevor Darrell (2014). "Fully convolutional networks for semantic segmentation". [2]