基于DenseNet结合迁移学习的胰腺囊性肿瘤分类方法

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