Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of information theory
Elements of information theory
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Scheduling for a Flexible Processing Network
Operations Research
Robust Real-Time Face Detection
International Journal of Computer Vision
Scheduling Algorithms
Handbook of Scheduling: Algorithms, Models, and Performance Analysis
Handbook of Scheduling: Algorithms, Models, and Performance Analysis
Liver segmentation using sparse 3D prior models with optimal data support
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Localization of 3D anatomical structures using random forests and discrete optimization
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Regression forests for efficient anatomy detection and localization in CT studies
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Deformable segmentation via sparse shape representation
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Robust learning-based annotation of medical radiographs
MCBR-CDS'09 Proceedings of the First MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Mining anatomical, physiological and pathological information from medical images
ACM SIGKDD Explorations Newsletter
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With the advance of whole-body medical imaging technologies, computer aided detection/diagnosis (CAD) is being scaled up to deal with multiple organs or anatomical structures simultaneously. Multiple tasks (organ detection/segmentation) in a CAD system are often highly dependent due to the anatomical context within a human body. In this paper, we propose a method to schedule multi-organ detection/segmentation based on information theory. The central idea is to schedule tasks in an order that each operation achieves maximum expected information gain. The scheduling rule is formulated to embed two intuitive principles: (1) a task with higher confidence tends to be scheduled earlier; (2) a task with higher predictive power for other tasks tends to be scheduled earlier. More specifically, task dependency is modeled by conditional probability; the outcome of each task is assumed to be probabilistic as well; and the scheduling criterion is based on the reduction of the summed conditional entropy over all tasks. The validation is carried out on two challenging CAD problems, multi-organ detection in whole-body CT and liver segmentation in PET-CT. Compared to unscheduled and ad hocscheduled organ detection/segmentation, our scheduled execution achieves higher accuracy with faster speed.