update readme

Author: fedorov

README.md

@@ -11,7 +11,7 @@ If this is the first time you hear about IDC, here are some highlights about wha
 
 The tutorial notebooks are located in the [notebooks](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks), and are organized in the following folders.
 
-## `getting_started`
+## [`getting_started`](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/getting_started)
 
 "Getting Started" notebooks are intended to introduce the users to IDC. We believe those notebooks are the best place to start using IDC. In this notebook series you will learn:
 * how IDC data is organized
@@ -20,22 +20,22 @@ The tutorial notebooks are located in the [notebooks](https://github.com/Imaging
 * how to use various visualization tools with IDC data
 * how to properly acknowledge data contributors and stay compliant with the usage license
 
-## `advanced_topics`
+## [`advanced_topics`](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/advanced_topics)
 
 Notebooks in this folder focus on topics that will require understanding of the basics, and aim to address more narrow use cases of IDC usage. Such topics include:
 * how to search clinical data accompanying IDC images and how to combine imaging and clinical metadata in your searches
 * how to use AWS-specific components for working with IDC data
 * how to deploy open source OHIF and Slim viewers using free Google Cloud resources
 
-## `collectons_demos`
+## [`collectons_demos`](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/collections_demos)
 
 This folders contains notebooks that demonstrate the usage of the data in the specific IDC collections. The notebooks in this folder will always have the prefix of the `collection_id` they correspond to, for easier navigation.
 
-## `pathomics`
+## [`pathomics`](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/pathomics)
 
 This folder is dedicated to the notebooks focused on the digital pathology (pathomics) applications. The use of DICOM standard is relatively new in digital pathology, and this field is being actively developed, thus a dedicated folder for this.
 
-## `deprecated`
+## [`deprecated`](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/deprecated)
 
 IDC is an actively evolving resource. As we develop new and improved capabilities, we improve our recommended usage practices, and may deprecate notebooks that are no longer maintained and may no longer work. You will find thse in the `deprecated` folder.
 
@@ -45,5 +45,4 @@ This directory is used for the maintenance of the repository to support testing
 
 # Support
 
-If you have any questions about the notebooks in this repository, please open a discussion thread in IDC user forum, or open the issue in this repository.
-
+If you have any questions about the notebooks in this repository, please open a discussion thread in [IDC user forum](https://discourse.canceridc.dev), or [open the issue in this repository](https://github.com/ImagingDataCommons/IDC-Tutorials/issues/new).

notebooks/README.md

@@ -1,37 +1 @@
-# IDC-Examples/notebooks
-The **notebooks** subdirectory of this repository contains a series of IPython notebooks that are intended to help you get started working with IDC hosted data.
-
-## Try these first
-* [Getting started notebook series](https://github.com/ImagingDataCommons/IDC-Examples/tree/master/notebooks/getting_started): Introduction to IDC data organization and main features
-* IDC segmentation primer: Experimenting with nnU-Net Segmentation of IDC Data [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ImagingDataCommons/IDC-Examples/blob/master/notebooks/IDC_segmentation_primer.ipynb)
-
-This notebook can be used as a source of small examples and useful bits for developing your own notebooks:
-* IDC Colab cookbook [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ImagingDataCommons/IDC-Examples/blob/master/notebooks/cookbook.ipynb)
-
-## Data Download and Exploration
-
-The following notebooks contain examples of how to download and explore IDC cohorts:
-
-* Introduction to IDC clinical data organization [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ImagingDataCommons/IDC-Examples/blob/master/notebooks/clinical_data_intro.ipynb)
-* LIDC exploration [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ImagingDataCommons/IDC-Examples/blob/master/notebooks/LIDC_exploration.ipynb)
-* Cohort download [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ImagingDataCommons/IDC-Examples/blob/master/notebooks/Cohort_download.ipynb)
-
-Furthermore, the Cohort Preparation notebook [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ImagingDataCommons/IDC-Examples/blob/master/notebooks/cohort_preparation.ipynb) contains a simple tutorial on how to get a cohort ready for your image processing applications (e.g., best practices for the conversion from DICOM to NRRD and NIfTI, pointers to pre-processing utilities).
-
-
-## Imaging Analysis AI
-
-The following notebooks contain examples of how IDC can be used to run AI-based medical imaging analysis pipelines on the cloud:
-
-* DeepPrognosis use case - replication study, 2 year survival score of NSCLC patients [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ImagingDataCommons/IDC-Examples/blob/master/notebooks/nsclc-radiomics/nsclc_radiomics_demo_release.ipynb)
-* Lung Nodules segmentation and prognosis use case - NSCLC patients nodules segmentation (nnU-Net) and prognosis (DeepPrognosis) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ImagingDataCommons/IDC-Examples/blob/master/notebooks/lung_nodules_demo.ipynb)
-* Thoracic Organs at Risk segmentation use case - NSCLC patients thoracic OAR segmentation (nnU-Net) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ImagingDataCommons/IDC-Examples/blob/master/notebooks/thoracic_oar_demo.ipynb)
-* Pathomics: Lung Tissue Data Exploration [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ImagingDataCommons/IDC-Examples/blob/master/notebooks/pathomics/lung_cancer_cptac_DataExploration.ipynb)
-* Pathomics: Lung Tissue Classification - training and applying a DL model for classification of tissue types [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ImagingDataCommons/IDC-Examples/blob/master/notebooks/pathomics/lung_cancer_cptac_TissueClassificationModel.ipynb)
-
-**N.B.**: since these demonstrations run in Google Colab, they highlight only a small part of what IDC can offer in terms of computational capability for imaging analysis. A more comprehensive experience of such tools can be explored, e.g., by experimenting with GCP Virtual Machines.
-
-To learn more about how to access the GCP virtual machines for free (using [free cloud credits](https://learn.canceridc.dev/introduction/requesting-gcp-cloud-credits)), please visit the [IDC user guide](https://learn.canceridc.dev/). 
-
-
-
+See main README file for details and explanation: https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master#readme.