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Dilated 3D CNN

时间: 2024-08-14 07:05:34 浏览: 55

Medical-Named-Entity-Rec-Based-on-Dilated-CNN:基于膨胀卷积神经网络（Divolution Convolutions）训练好的医疗命名实体识别工具

基于扩张神经网络（Divolved Convolutions）训练好的医疗领域的命名实体识别工具，这里主要引用模型源码，以及云部署方式供大家交流学习。环境阿里云服务器：Ubuntu 16.04 Python版本：3.6 Tensorflow：1.5 第一步：来一个Flask实例，并跑起来：使用的是Pycharm创建自带的Flask项目，xxx.py from flask import Flask app = Flask(__name__) @app.route('/') def hello_world(): return 'Hello World!' if __name__ == '__main__': app.run() 执行python xxx.py就可以运行在浏览器中测试若直接在dos窗口中：输入命令也可测试。第二部：服务器配置服务器python版本为3.x 安装pi

Dilated Convolutional Neural Networks (Dilated CNNs), also known as atrous convolutions or convolution with holes, are a type of deep learning architecture that extends standard convolutional layers by introducing gaps between the filter weights. These gaps allow the network to maintain its receptive field while increasing the spatial resolution of the feature maps without reducing the number of filters or requiring more computation. In a dilated 3D CNN[^4], each kernel moves through the input volume not only in the depth but also along the height and width dimensions, with dilation factors applied at regular intervals. This enables the model to capture long-range dependencies within volumetric data, such as medical images or videos, without losing spatial details due to pooling operations. For example, consider a 3x3x3 kernel with a dilation rate of 2: ```python from keras.layers import Conv3D, MaxPooling3D # Dilated Convolution dilated_conv = Conv3D(filters=64, kernel_size=(3, 3, 3), dilation_rate=(2, 2, 2), activation='relu') # Without dilation, the kernel would touch every pixel; with dilation, it skips one. # Output size will be same or slightly larger than input size depending on padding. # Optional downsampling using MaxPooling3D can follow for further context aggregation max_pool = MaxPooling3D(pool_size=(2, 2, 2)) # The combined layer sequence captures both local and distant features in the 3D volume. ```

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