An Information Fusion Framework for Robust Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian multiple instance learning: automatic feature selection and inductive transfer
Proceedings of the 25th international conference on Machine learning
Multiple-Instance Learning Improves CAD Detection of Masses in Digital Mammography
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
Active Scheduling of Organ Detection and Segmentation in Whole-Body Medical Images
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Cross Modality Deformable Segmentation Using Hierarchical Clustering and Learning
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
The Journal of Machine Learning Research
Robust MR spine detection using hierarchical learning and local articulated model
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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The field of medical imaging has shown substantial growth over the last decade. Even more dramatic increase was observed in the use of machine learning and data mining techniques within this field. In this paper, we discuss three aspects related to information mining in the domain of medical imaging: the target user groups (for whom), the information to mine (what), and technologies to enable mining (how). Specifically, we focus on three types of information: anatomical, physiological and pathological, and present use cases for each one of them. Furthermore, we introduce representative methods and algorithms that are effective for solving these problems. We conclude the paper by discussing some major trends in the related domains for the coming decade.