计算机科学
增采样
变通办法
分割
人工智能
编码器
深度学习
计算机工程
卷积神经网络
网络体系结构
计算复杂性理论
宏
人工神经网络
图像(数学)
图像分割
模式识别(心理学)
机器学习
算法
计算机网络
操作系统
程序设计语言
作者
Khan, Tariq M.,Naqvi, Syed S.,Meijering, Erik
出处
期刊:Cornell University - arXiv
日期:2021-12-21
标识
DOI:10.48550/arxiv.2112.11065
摘要
Recent progress in encoder-decoder neural network architecture design has led to significant performance improvements in a wide range of medical image segmentation tasks. However, state-of-the-art networks for a given task may be too computationally demanding to run on affordable hardware, and thus users often resort to practical workarounds by modifying various macro-level design aspects. Two common examples are downsampling of the input images and reducing the network depth to meet computer memory constraints. In this paper we investigate the effects of these changes on segmentation performance and show that image complexity can be used as a guideline in choosing what is best for a given dataset. We consider four statistical measures to quantify image complexity and evaluate their suitability on ten different public datasets. For the purpose of our experiments we also propose two new encoder-decoder architectures representing shallow and deep networks that are more memory efficient than currently popular networks. Our results suggest that median frequency is the best complexity measure in deciding about an acceptable input downsampling factor and network depth. For high-complexity datasets, a shallow network running on the original images may yield better segmentation results than a deep network running on downsampled images, whereas the opposite may be the case for low-complexity images.
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