An end-to-end multitask model with shared encoder, separate decoders, batch-Wasserstein loss, and soft attention module reports better performance than prior segmentation and saliency methods on the MICCAI robotic instrument dataset.
In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)
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Learning Where to Look While Tracking Instruments in Robot-assisted Surgery
An end-to-end multitask model with shared encoder, separate decoders, batch-Wasserstein loss, and soft attention module reports better performance than prior segmentation and saliency methods on the MICCAI robotic instrument dataset.