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Found 5 papers, 2 papers with code
Brain Tumor Detection Based on a Novel and High-Quality Prediction of the Tumor Pixel Distributions
In each step, the prediction result helps to identify/remove tumor-free slices, increasing the tumor information density in the remaining data to be applied to the next step.
Coupling Global Context and Local Contents for Weakly-Supervised Semantic Segmentation
Specifically, a flexible context aggregation module is proposed to capture the global object context in different granular spaces.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation
For the systems to be applicable in practice, a good The research on developing CNN-based fully-automated Brain-Tumor-Segmentation systems has been progressed rapidly.
A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation
The work presented in this paper is to propose a reliable high-quality system of Convolutional Neural Network (CNN) for brain tumor segmentation with a low computation requirement.
SdcNet: A Computation-Efficient CNN for Object Recognition
In this paper, a computation-efficient convolutional module, named SdcBlock, is proposed and based on it, the convolution network SdcNet is introduced for object recognition tasks.
