How to implement Medical Image Segmentation with Multi-GPU training in Tensorflow

Today, I will talk to you about how to achieve multi-GPU training medical image segmentation cases in Tensorflow. Many people may not know much about it. In order to make you understand better, the editor has summarized the following content for you. I hope you can get something according to this article.

Today I will explain in detail how to implement it with specific examples.

1. Download the dataset

In order to facilitate everyone to learn, I will share the preprocessed image with you. The image I use is a cell segmentation image. Baidu Cloud Link: https://pan.baidu.com/s/1T0hKE0uvWkDHnK-a8p1bGg password: g1u4.

2. Data preparation

Download the data, as shown in the figure. Don't worry, I have written all these image paths in csv format. All we have to do is put the data in the D:\ Data\ directory. Put the two csv files in the same directory as our training script.

3. Set parameters and train

All we need to do is set the batch_size and num_gpus parameters, for example, when I train with two GTX1080, set the batch_size to 4 _ numm _ pussy _ 2. When the setup is complete, we run the script training directly.

In order to make it easier for you to learn more efficiently, I sorted out the code and updated it to Github, address: https://github.com/junqiangchen/MutiltGPU_Unet2d

If you think this project is good, I hope you can give me a Star and Fork, so that more people can learn. If you have any questions, please feel free to leave me a message and I will reply in time.

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