ASCNet learns per-pixel adaptive dilation rates via a 3-layer convolution structure to produce scale-appropriate receptive fields, yielding higher segmentation accuracy than fixed dilated CNNs on two medical image datasets.
In: Proceedings of the IEEE conference on computer vision and pattern recognition
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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.
citing papers explorer
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ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning
ASCNet learns per-pixel adaptive dilation rates via a 3-layer convolution structure to produce scale-appropriate receptive fields, yielding higher segmentation accuracy than fixed dilated CNNs on two medical image datasets.
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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.