manideep2510 / melanoma_segmentation

About

This code is implementation of the paper "Automatic skin lesion segmentation with fully convolutional-deconvolutional networks" on ISIC 2017: Skin Lesion Analysis Towards Melanoma Detection, Part 1: Lesion Segmentation.

This code is implemented using Keras and Tensorflow frameworks.

Data set folders

• trainx - 2000 training RGB images of effected parts of skin resized to 192 x 256.
• trainy - 2000 segmented (Ground Truth) images of images in train resized to 192 x 256.
• validationx - 150 validation images of effected parts of skin resized to 192 x 256.
• validationy - 150 segmented (Ground Truth) images of images in train resized to 192 x 256.
• testx - 600 test images of effected parts of skin resized to 192 x 256.
• testy - 600 segmented (Ground Truth) images of images in train resized to 192 x 256.

All the images in all the folders are resized to 192 x 256 size.

If you want to resize the original images in data set then use the reshape.py script by doing minute changes in it like changing folder names and number of images.

Here you dont need to run the reshape.py on images as the images in the folders are already resized to our required dimensions i.e, 192 x 256 (Can see the reason for this in the original paper)

Code

• reshape.py - code to reshape all the images in a folder to 192 x256 images.(No need to run in this case as the data in this repository is already reshaped)
• melanoma_segmentation.ipynb - Jupyter notebook with all the code for convolution and deconvolution layers, training, etc.
• load_batches.py - (Don't use this)load all the images in a folder to batchs of batch size 16.(No need to run this script as another function which do the same operation as this is created in the jupyter notebook)

Errors

• (Solved)An error with the axis in the tf.reduce-sum() operation in the jaccard_loss() function which I created.

• (Solved)There is some problem with dividing the input images into batches. Because of which a dimentional error is occuring.

• (Solved)There is a problem with the function unpool() which need to be fixed.

  Created new function called UnPooling2x2ZeroFilled() instead of unpool() for unpooling layer during deconvolution which is working fine.

Note

• The code after the line _, _, parameters = model(X_train, Y_train, X_test, Y_test) after the model() function in the main jupyter notebook is just spare code and some testing so don't run the code after the above mentioned line of code

• In melanoma_segmentation.ipynb, if you are running the jupyter notebook on your pc offline, don't run the cells with codes ! git clone and cd. Run there cells only if you are running this notebook in Google Colab.