Added first draft of template for documentation of prediction algorithm

docs/templates/algorithm_doc_template.md

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+<!-- Add the algorithm's name as the title -->
+# Algorithm name
+
+## Summary
+<!-- A brief (2-3 sentences) summary of the algorithm -->
+
+## Source
+<!-- Where does the algorithm come from. If not a Data Science Bowl entry, adjust the template accordingly. -->
+**Author:**     </br>
+**Repository:**     </br>
+<!-- Comment whether the algorithm was an entry to a challenge (e.g. Data Science Bowl) or similar. 
+Also mention Placement. -->
+
+## License
+<!-- License the algorithm is under. -->
+
+
+## Prerequisites
+<!-- summary on dependencies needed to run the algorithm -->
+
+| Dependency |   Name   | Version  |
+|------------|----------|----------|
+| Language   | Python   |          | <!-- If not Python 3.5+ please describe steps to port into Python 3.5+ below -->
+| ML engine  |          |          | <!-- e.g. Keras -->
+| ML backend |          |          | <!-- e.g. Tensorflow -->
+| OS         |          |          | <!-- Include all OS that were tested -->
+| Processor  | CPU      | (yes/no) |
+|            | GPU      | (yes/no) |
+| GPU driver | CUDA     |          |
+|            | cuDNN    |          |
+
+
+**Dependency packages:**
+<!-- List dependency packages (e.g. numpy) with version. If using python use requirements.txt syntax for pip. That makes
+ easy for the developers. -->
+````
+
+````
+
+
+## Algorithm design
+<!-- Describe the model and its architecture in detail. -->
+
+### Preprocessing
+
+### Nodule detection
+
+### Prediction of cancer probability
+
+## Trained model
+
+**Source:**     </br>
+
+**Usage instructions:**     </br>
+
+## Model Performance
+
+### Training- / prediction time
+<!-- If the specs of multiple test systems are known copy/paste the snippet below -->
+
+**Test system:**     </br>
+
+| Component | Spec  | Count |
+|-----------|-------|-------|
+| CPU       |       |       |
+| GPU       |       |       |
+| RAM       |       |       |
+
+**Training time:**  </br>
+**Prediction time:** </br>
+
+### Model Evaluation
+<!-- State accuracy and other evaluation metrics for datasets the algorithm was tested on. -->
+
+**Dataset:**    </br>
+
+| Metric   | Score |
+|----------|-------|
+| Accuracy |       |
+
+## Use cases
+<!-- List strengths and weaknesses of the algorithm. -->
+
+### When to use this algorithm
+
+ - 
+ -
+ -
+
+### When to avoid this algorithm
+
+ - 
+ -
+ -
+
+## Adaptation into Concept To Clinic
+
+### Porting to Python 3.5+
+<!-- Comment on possible problems/solutions for porting the algorithm to Python 3.5+ -->
+
+### Porting to run on CPU and GPU
+<!-- To be able to support a larger variety of systems, is it possible to make the model run on CPU and GPU? -->
+
+### Improvements on the code base
+<!-- What improvements on the code base can be made? (e.g. to increase performance, readability, etc.) -->
+
+### Adapting the model
+<!-- What parts of the model are useful for the project? What changes could be made to make it more useful 
+     for our product? -->
+
+## Comments
+<!-- In this sections contributors can express their opinion on the algorithm -->
+
+## References
+<!-- Links to the official documentation and other ressources referenced in this document -->