Data Science Bowl 2018

This project is a tool for detecting cells in medical images. It is based on the following Kaggle challenge.

## Project structure

TODO: Add structure

Install the project

For this, run the following command:

conda install --file environement.txt

Then, run:

python setup.py develop

## Workflow

1. Get and process the data
2. Train the model
3. Postprocess the predictions

Get the data

Once you have installed the project dependencies, you will have access to the Kaggle official CLI. Run kaggle --help to confirm this.

To get the data, run:

kaggle competitions download -c data-science-bowl-2018

You will need to accept the competition conditions and create an API key first.

## Running TensorBoard

To run TensorBoard (a great visualization tool),

tensorboard --logdir=/path/to/tb_logs

## Tips

• It is better to use skimage instead of numpy for reading and processing images.
• It is even better to use Keras built-in image processing capabilities.
• Log the various image sizes when debugging your data processing pipeline.
• Use only one image when debugging your data pipeline (so that to avoid loading all the data multiple times).

## Sources and useful links

• U-net Kaggle kernel: https://www.kaggle.com/keegil/keras-u-net-starter-lb-0-277
• U-net paper: https://arxiv.org/abs/1505.04597
• Upsampling basics: https://www.cs.toronto.edu/~guerzhoy/320/lec/upsampling.pdf