How to segment color image(Skin lesion) with Unet and transfert learning?

Hello, I am a beginner in deep learning! So,I have a medical image database (Skin lesion) to segment with U-net and transfer learning! For that, I downloaded " u-net-release-2015-10-02.tar.gz"(https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/) but I don't know how to segment my database!

Please, if you can help and guide me to segment with Unet and how to use transfer learning?

[mohd akmal masud]

First you have to groundtruth labeling your data.

Defind your data,

Answer 1

[Prasanna]

Hi Samia,

To segment skin lesion images using Unet, you can follow the below steps:

• Setup the MATLAB environment, and extract the U-Net files from the contents of 'u-net-release-2015-10-02.tar.gz'.
• Organize your images and masks into two folders: one for the images and another for the masks. Then, Preprocess the data and split data for training and validation
• Create a U-Net model using built-in functions such as the 'unet' function. To use transfer learning, you can modify the U-Net architecture to include a pre-trained network such as the 'resnet50' as the encoder. Train the model with appropriate training options to segment skin lesions.

For more information on U-Net and transfer learning to U-Net, refer the following resources:

• unet: https://www.mathworks.com/help/vision/ref/unet.html
• Transfer learning on U-Net Created in MATLAB: https://www.mathworks.com/matlabcentral/answers/506510-how-to-use-transfer-learning-on-u-net-created-in-matlab
• Transfer learning with deep network designer: https://www.mathworks.com/help/deeplearning/ug/transfer-learning-with-deep-network-designer.html
• Transfer learning: https://blogs.mathworks.com/pick/2017/02/24/deep-learning-transfer-learning-in-10-lines-of-matlab-code/

Hope this helps!