##### Chester, the AI Radiology Assistant (MILA Medical, MLMED, Montreal)
* Demo for an X-Ray based diagnostics (MILA Medical, MLMED)
* paper: Joseph Paul Cohen, Paul Bertin, Vincent Frappier. “Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System”. Jan. 2019
https://arxiv.org/abs/1901.11210
* Machine Learning and Medicine Lab (MLMED), MILA Medical (Montreal Institute for Learning Algorithms)
* Tool with Web Interface: https://mlmed.org/tools/xray/
* Code: https://github.com/mlmed/chester-xray
* Core components already described:
"The system contains three main components":
-**Out Of Distribution error** (*transferability / compatibility*): This is a heatmap showing how the image differs from our training data. If the heatmap is too bright then the image is very different from our training data and the model will likely not work. We will prevent an image from being processed if it is not similar enough to our training data in order to prevent errors in predictions.
-**Predictive image regions** (*explainability*): The brighter each pixel is in the heatmap the more influence it can have on the predictions. If the color is bright it means that a change in these pixels will change the prediction.
-**Disease Predictions** (*uncertainty* representation): A probability indicating how likely the image contains the disease. 50\% means the network is not sure.
