Research on Network Compression for Similarity Measurement of Medical Image Features
In order to improve the performance of application of deep learning and network transfer to the medical image field,the peak signal-to-noise ratio method was used to measure the similarity of feature activation graphs of layers,so as to evaluate the importance of intra-layer and inter-layer channels.Firstly,the medical image was preprocessed,and the peak signal-to-noise ratio method was used to mea-sure the similarity between the preprocessed image and the confluence layer of each node of the network to determine the transfer network depth.Secondly,the feature similarity measurement was carried out on the features within the layer and between different layers,and they were sorted according to the measure-ment results.Again,the network was compressed by the feature metric results and the desired compression factor.The experimental results showed that using feature similarity measure to prune the transfer network channel can effectively compress the network with a small performance degradation.Therefore,using the peak signal-to-noise ratio method to measure the feature similarity can become the judgment basis for channel pruning.
medical imagesimilarity measurementchannel pruningnetwork compression
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