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+# Tutorials
+
+For getting a deeper understanding of the package, we have provided with multiple google collabs which showcases
+different functionalities of med-imagetools
+
+## Tutorial 1: Forming Dataset with med-imagetools Autopipeline
+
+Autopipeline is one of the main features of med-imagetools which turns messy raw data and processes it taking into account
+the different relationships between different modalities. This notebook gives a demo of how the autopipeline can be used
+
+[![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/skim2257/tcia_samples/blob/main/notebooks/Tutorial_1_Forming_Dataset_with_Med_Imagetools.ipynb)
+
+## Tutorial 2: Machine Learning with med-imagetools and torchio
+
+This tutorial will showcase how we can form Machine Learning pipeline with the help of med-imagetools and torchio. In this notebook we will go through segmentation of body from head and neck cancer dataset
+
+[![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/skim2257/tcia_samples/blob/main/notebooks/Tutorial_2_Machine_Learning_with_Med_Imagetools_and_torchio.ipynb)