--- a
+++ b/README.md
@@ -0,0 +1,123 @@
+
+![Logo of the project](https://www.ai4media.eu/wp-content/uploads/2021/04/Twitter_Building-Interpretable-AI-for-Digital-Pathology-1024x575.png)
+
+**Presented by:**
+
+- Mara Graziani
+    - Pre-doc researcher with HES-SO Valais & UniGe
+    - mara.graziani@hevs.ch
+
+
+- Guillaume Jaume
+    - Pre-doc researcher with EPFL & IBM Research 
+    - gja@zurich.ibm.com  
+
+
+- Pushpak Pati 
+    - Pre-doc researcher with ETH & IBM Research
+    - pus@zurich.ibm.com
+    
+Welcome to the AMLD 2021 workshop about **Building Interpretable AI for Digital Pathology**. This hands-on session is created with the purpose of showcasing multiple ways in which developers may interpret automated decision making for digital pathology. 
+
+<!---
+Deep learning algorithms may hide inherent risks such as the codification of biases, weak accountability and bare transparency of the decision-making. Giving little insights about their final output, deep models are perceived by clinicians as black-boxes. 
+Clinicians, on their side, are the sole people legally responsible and accountable for the diagnoses and treatment decisions. 
+Providing justifications for automated predictions may have a positive impact of computer aided diagosis, for example, by increasing the uptake of automated support within the decision making process.
+-->
+
+## Schedule 
+
+The workshop will take place on the 27th of April from 9:00 to 12:00 CET. 
+
+| Time      | Title                                | Presenter              |                  
+|-----------|--------------------------------------|------------------------|
+| 9:00-9:05 | Welcome                                | Guillaume Jaume        |
+| 9:05-9:25 | Introduction to Digital Pathology      | Prof. Dr. Inti Zlobec  |
+| 9:25-9:45 | Introduction to Interpretability       | Mara Graziani          |
+| 9:45-9:55 | Break 1                                | -                      |
+| 9:55-10:35 | Hands-on session 1: CNNs & Concept Attribution | Mara Graziani |
+| 10:35-10:45 | Break 2                              | -                      |
+| 10:45-11:55 | Hands-on session 2: Graph-based interpretability | Guillaume Jaume, Pushpak Pati |
+| 11:55-12:00 | Closing remarks                      | Pushpak Pati           |
+
+## What to do before the workshop: 
+
+The participants need to bring their own laptop with basic development setup. We recommend testing the following steps before starting the workshop:
+
+- Cloning the repository 
+
+```
+>> git clone https://github.com/maragraziani/interpretAI_DigiPath.git && cd interpretAI_DigiPath
+```
+
+- Launch Jupyter Notebook 
+
+```
+>> jupyter notebook
+```
+
+- Test opening one of the notebooks in Colab, e.g., [hands-on-session-1/feature_attribution_demo.ipynb](https://github.com/maragraziani/interpretAI_DigiPath/blob/main/hands-on-session-1/feature_attribution_demo.ipynb).
+
+
+## Content
+
+Deep learning algorithms may hide inherent risks such as the codification of biases, weak accountability and bare transparency of the decision-making. Giving little insights about their final output, deep models are perceived by clinicians as black-boxes. 
+Clinicians, on their side, are the sole people legally responsible and accountable for the diagnoses and treatment decisions. 
+Providing justifications for automated predictions may have a positive impact of computer aided diagosis, for example, by increasing the uptake of automated support within the decision making process.
+
+<!---
+You have a deep learning model, may it be a Convolutional Neural Network (CNN) or a graph-network. 
+Your model works on high magnification croppings of histopathology input images, also called patches or tiles. 
+The main task is the classification of patches containing evidence of tumor from those without. 
+This is modeled as a binary classification task with one output node and a logistic regression activation function, where 1 corresponds to the "tumor" class and 0 to the non-tumor class. 
+
+Common theme:
+<li> histopathology image input: you may use any of your histopathology datasets, or public data collections 
+<li> continuous or categorical output: a single node output is used for demonstration purposes. Similar applications can be derived for multiple node outputs, e.g. multi-class classification tasks. 
+-->
+
+### Part 1: Interpreting 2D CNNs 
+
+This part focuses on understanding the decision process on ConvNets with:
+* feature attribution: Class Activation Mapping (CAM) and its Gradient-weighted version 
+* concept attribution: Regression Concept Vectors (RCV)
+
+You will work on the implementation of Gradient-weighted Class Activation Mapping (Grad-CAM) as an example of feature attribution.
+RCVs will be applied to generate complementary explanations in terms of clinically relevant measures such as nuclei area and appearance. 
+
+The notebooks and instructions for this part are in the folder 2DCNNs. 
+
+### Part 2: Explainable Graph-based Representations in Digital Pathology
+
+The second part of this tutorial will guide you to build **interpretable entity-based representations** of tissue regions. The motivation starts from the observation that cancer diagnosis and prognosis is driven by the distribution of histological entities, *e.g.,* cells, nuclei, tissue regions. A natural way to  characterize the tissue is to represent it as a set of interacting entities, *i.e.,* a graph. Unlike most of the deep learning techniques operating at pixel-level, the entity-based analysis preserves the notion of histopathological entities, which the pathologists can relate to and reason with. Thus, explainability of the entity-graph based methodologies can be interpreted by pathologists, which can potentially lead to build trust and adoption of AI in clinical practice. Notably, the produced explanations in the entity-space are better localized, and therefore better discernible.
+
+  
+## Reference papers
+
+
+```
+@article{graziani2020,
+    title = "Concept attribution: Explaining {{CNN}} decisions to physicians",
+    author = "Mara Graziani, Vincent Andrearczyk, Stephane Marchand-Maillet, Henning Müller"
+    booktitle = "Computers in Biology and Medicine",
+    pages = "103865",
+    year = "2020",
+    doi = "https://doi.org/10.1016/j.compbiomed.2020.103865"
+}
+
+@inproceedings{pati2021,
+    title = "Hierarchical Graph Representations in Digital Pathology",
+    author = "Pushpak Pati, Guillaume Jaume, Antonio Foncubierta, Florinda Feroce, Anna Maria Anniciello, Giosuè Scognamiglio, Nadia Brancati, Maryse Fiche, Estelle Dubruc, Daniel Riccio, Maurizio Di Bonito, Giuseppe De Pietro, Gerardo Botti, Jean-Philippe Thiran, Maria Frucci, Orcun Goksel, Maria Gabrani",
+    booktitle = "arXiv",
+    url = "https://arxiv.org/abs/2102.11057",
+    year = "2021"
+} 
+
+@inproceedings{jaume2021,
+    title = "Quantifying Explainers of Graph Neural Networks in Computational Pathology",
+    author = "Guillaume Jaume, Pushpak Pati, Behzad Bozorgtabar, Antonio Foncubierta-Rodríguez, Florinda Feroce, Anna Maria Anniciello, Tilman Rau, Jean-Philippe Thiran, Maria Gabrani, Orcun Goksel",
+    booktitle = "IEEE CVPR",
+    url = "https://arxiv.org/abs/2011.12646",
+    year = "2021"
+} 
+```