TY - JOUR
AU - Shen,  Dinggang
AB - Machine Vision and Applications (2013) 24:1327–1329 DOI 10.1007/s00138-013-0543-8 EDITORIAL Pingkun Yan · Kenji Suzuki · Fei Wang · Dinggang Shen Published online: 31 August 2013 © Springer-Verlag Berlin Heidelberg 2013 There is no doubt that medical imaging has become indis- tion, image registration, image fusion, image-guided therapy, pensable in disease diagnosis and therapy. With advances in image annotation, and image database retrieval. medical imaging, new imaging modalities and methodolo- The main aim of this special issue is to help advance the gies such as cone-beam/multi-slice CT, 3D ultrasound imag- scientific research within the broad field of machine learning ing, tomosynthesis, diffusion-weighted magnetic resonance in medical imaging. The special issue was planned in con- imaging (MRI), positron-emission tomography (PET)/CT, junction with the International Workshop on Machine Learn- electrical impedance tomography, and diffuse optical tomog- ing in Medical Imaging 2011 [1]. Being the first workshop raphy, the imaging information available for clinical decision on this topic, it has been successfully held together with the making has been erupting. To take full advantage of medical International Conference on Medical Image Computing and imaging, new algorithms and methods are demanded in the Computer-Assisted Intervention (MICCAI) for four consec- medical imaging field to better 
TI - Machine learning in medical imaging
JF - Machine Vision and Applications
DO - 10.1007/s00138-013-0543-8
DA - 2013-10-01
UR - https://www.deepdyve.com/lp/springer-journals/machine-learning-in-medical-imaging-0yC2bB0VX0
SP - 1327
EP - 1329
VL - 24
IS - 7
DP - DeepDyve
ER -