Deep Learning Based Brain Tumor Segmentation: A Survey
원문 초록
Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we use this survey to provide a comprehensive study of recently developed deep learning based brain tumor segmentation techniques. More than 100 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality processes. We also provide insightful discussions for future development directions.
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MUPAX: Multidimensional Problem Agnostic eXplainable AI
Transferring Styles for Reduced Texture Bias and Improved Robustness in Semantic Segmentation Networks
A Robust Feature Downsampling Module for Remote Sensing Visual Tasks
Flexible Fusion Network for Multi-modal Brain Tumor Segmentation
Crosslink-Net: Double-Branch Encoder Network via Fusing Vertical and Horizontal Convolutions for Medical Image Segmentation
Transfer learning for automatic brain tumor classification Using MRI Images.
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