As I have received the emails from some readers about the "pulmonary nodule intelligent diagnosis" project in my Github these days, I written to answer some of these questions. Letters to readers
This code is to deal with Tianchi Dataset, and train lung nodule segmentation based on convolutional neural network using U-Net deep learning framework.
This code is to deal with Tianchi Dataset, and train the algorithm for image classification (such as CNN) to classify the suspected nodules, the suspected pulmonary nodule isWhether the real probability of pulmonary nodules.
Pulmonary_nodules_AI_diagnosis is designed and implemented by Yingyi Wu yywu@szucla.org.
This repository is not intended to be an out of the box solution for the DSB challenge. It will not run out-of-the-box without editing. That was not it's intention. The tutorial was put together rapidly by several people working in tandem and the code herein is a collection of the code they used to produce the tutorial found on the DSB website.
The intent behind this tutorial was to presented a series of steps that can be followed as a starting point for competitors. Our hope is that this can save competitors time in framing the problem and that they can lift some of this code to speed up their own solution generation. We expect that the competitors efforst will supercede this tutorial in short order--which is, of course, the point of the competition.
Thanks for participating and helping to advance cancer diagnosis!
Optional data could be downloaded from the following links.
evaluation script: the LUNA16 evaluation script can be found here. The script could be used to locally evaluate the system for development purposes. More info is available here. [updated: 17th June 2016]
这个工程用于托管我分享帖中涉及的代码，有部分病人的文件作为示例。
存放了那几个csv文件
npy里存放的是提取出来的nodule_cubes
存放的是根据annotations.csv文件生成的slices和对应的GroundTruth，可用于训练2D-Unet
