DeepLung论文笔记

DeepLung论文笔记

论文主要思路

DeepLung contains two parts,nodule detection and classification.

3D Faster R-CNN is designed for nodule detection
gradient boosting machine(GBM) with 3D dual path network (DPN) features is designed for nodule classification

论文框架

3D Faster R-CNN for Nodule Detection

The model employs 3 anchors and multitask
learning loss, including coordinates (x; y; z) and
diameter d regression, and candidate box classification.
The numbers in boxes are feature map sizes in
the format (#slices*#rows*#cols*#maps). The numbers
above the connections are in the format (#filters,slices#rows*#cols).

损失函数loss为

pi该i点是否为节点
ti是相关坐标组，d是直径

对ground truth有：

Gradient Boosting Machine with 3D Dual Path Net Feature for Nodule Classification

用到DPN网络模型

整个网络框架为

Gradient Boosting Machine(GBM）是什么？

gradient boosting machine (GBM) is a superior
method to build an advanced classifier from these
features

GBM详细解释

For nodule detection,we design a 3D Faster R-CNN with U-net-like encoder-decoder structure to detect suspicious nodules. Then we input the detected nodules into the nodule classification network.
The 3D deep dual path network is designed to extract
features. Further, gradient boosting machine with different features combined together to achieve the state-of-the-art performance