Search Results for author:
Found 6 papers, 5 papers with code
PnPNet: Pull-and-Push Networks for Volumetric Segmentation with Boundary Confusion
Precise boundary segmentation of volumetric images is a critical task for image-guided diagnosis and computer-assisted intervention, especially for boundary confusion in clinical practice.
Implicit Shape Modeling for Anatomical Structure Refinement of Volumetric Medical Images
In this paper, a unified framework is proposed for 3D shape modelling and segmentation refinement based on implicit neural networks.
Semantic Difference Guidance for the Uncertain Boundary Segmentation of CT Left Atrial Appendage
In this paper, we have modeled relative relations between the LA and LAA via deep segmentation networks for the first time, and introduce a new LA & LAA CT dataset.
Learning with Explicit Shape Priors for Medical Image Segmentation
Hence, in our work, we proposed a novel shape prior module (SPM), which can explicitly introduce shape priors to promote the segmentation performance of UNet-based models.
The Deep Learning Compiler: A Comprehensive Survey
In this paper, we perform a comprehensive survey of existing DL compilers by dissecting the commonly adopted design in details, with emphasis on the DL oriented multi-level IRs, and frontend/backend optimizations.
swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight
Second, we propose a set of optimization strategies for redesigning a variety of neural network layers based on Caffe.
