基于DenseNet结合迁移学习的胰腺囊性肿瘤分类方法

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田慧,武杰,边云,张志伟,邵成伟

Classification of Pancreatic Cystic Tumors Based on DenseNet and Transfer Learning

TIAN Hui,WU Jie,BIAN Yun,ZHANG Zhiwei,SHAO Chengwei

表1 DenseNet161网络结构

Table 1 DenseNet161 network structure

Layers	DenseNet161
Convolution	7×7 conv
Pooling	3×3 max pool
Dense Block 1	$\left[ \begin{matrix} 1\times 1\ \text{conv} \\ 3\times 3\ \text{conv} \\ \end{matrix} \right]\times 6$
Transition Layer 1	1×1 conv, 2×2 average pool
Dense Block 2	$\left[ \begin{matrix} 1\times 1\ \text{conv} \\ 3\times 3\ \text{conv} \\ \end{matrix} \right]\times 12$
Transition Layer 2	1×1 conv, 2×2 average pool
Dense Block 3	$\left[ \begin{matrix} 1\times 1\ \text{conv} \\ 3\times 3\ \text{conv} \\ \end{matrix} \right]\times 36$
Transition Layer 3	1×1 conv, 2×2 average pool
Dense Block 4	$\left[ \begin{matrix} 1\times 1\ \text{conv} \\ 3\times 3\ \text{conv} \\ \end{matrix} \right]\times 24$
Classification Layer	7×7 global average pool, fully-connected, softmax