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Recognizing Focal Liver Lesions in Contrast-Enhanced Ultrasound with Discriminatively Trained Spatio-Temporal Model
The aim of this study is to provide an automatic computational framework to
assist clinicians in diagnosing Focal Liver Lesions (FLLs) in
Contrast-Enhancement Ultrasound (CEUS). We represent FLLs in a CEUS video clip
as an ensemble of Region-of-Interests (ROIs), whose locations are modeled as
latent variables in a discriminative model. Different types of FLLs are
characterized by both spatial and temporal enhancement patterns of the ROIs.
The model is learned by iteratively inferring the optimal ROI locations and
optimizing the model parameters. To efficiently search the optimal spatial and
temporal locations of the ROIs, we propose a data-driven inference algorithm by
combining effective spatial and temporal pruning. The experiments show that our
method achieves promising results on the largest dataset in the literature (to
the best of our knowledge), which we have made publicly available.Comment: 5 pages, 1 figure
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