We present a novel deep learning architecture for probabilistic future prediction from video. We predict the future semantics, geometry and motion of complex real-world urban scenes and use this representation to control an autonomous vehicle. This work is the first to jointly predict ego-motion, static scene, and the motion of dynamic agents in a probabilistic manner, which allows sampling consistent, highly probable futures from a compact latent space. Our model learns a representation from RGB video with a spatio-temporal convolutional module. The learned representation can be explicitly decoded to future semantic segmentation, depth, and optical flow, in addition to being an input to a learnt driving policy. To model the stochasticity of the future, we introduce a conditional variational approach which minimises the divergence between the present distribution (what could happen given what we have seen) and the future distribution (what we observe actually happens). During inference, diverse futures are generated by sampling from the present distribution.
Lymphangiomyomas are relatively rare, benign neoplasms. Many patients present with symptoms including effusions, and some cases are incidentally detected. Surgical excision is the treatment of choice, but because of its location, complete surgical resection of a lymphangioma can be technically difficult, and recurrent cases can present with symptoms including effusions. 99mTc-sulfur colloid scan can be used to confirm the leak and nature of the effusion fluid. Here, we present an 8-year-old girl with recurrent pleural and pericardial effusions after lymphocele excision and total pericardiectomy. 99mTc-sulfur colloid lymphoscintigraphy was done to rule out secondary chylopericardium.
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