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- W2073090634 abstract "The objective of this work is to develop a technique that is reliable, adaptive, versatile to solve the problem of region detection for a relatively wide class of medical images. Learning is essential in approaching this objective. In order to fully use the properties of the medical images and obtain a high efficiency, we compute a binary visual attention map which contains the region of interest as well as other things. The learning takes place in two stages: (1) learning for automatic selection of threshold values; (2) learning for automatic selection of the region of interest from candidate regions in the attention map. The result from the second stage is evaluated based on a learned cost measure and the outcome is fed back to the first stage when necessary. This feedback enhances the reliability of the entire system. Experiments have been conducted to approximately locate the endocardium boundaries of the left and right ventricles from gradient-echo MR images." @default.
- W2073090634 created "2016-06-24" @default.
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- W2073090634 date "1994-05-11" @default.
- W2073090634 modified "2023-09-22" @default.
- W2073090634 title "<title>Fully automatic ventricle detection from cardiac MR images using machine learning</title>" @default.
- W2073090634 doi "https://doi.org/10.1117/12.175086" @default.
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