Kaggle: 2018 Data Science Bowl

Find the nuclei in divergent images to advance medical discovery

Top10%: 358 place

Installation

#anaconda
#install anaconda
#pytorch
$conda install pytorch torchvision -c pytorch
$git clone https://github.com/pedrodiamel/pytorchvision.git
$cd pytorchvision
$python setup.py install
$git clone https://github.com/pedrodiamel/datasciencebowl-2018.git
$cd clone datasciencebowl-2018
$pip install -r installation.txt

Download Kaggle dataset

# loader dataset 
kaggle competitions download -c data-science-bowl-2018    
# relabel
$ git clone https://github.com/lopuhin/kaggle-dsbowl-2018-dataset-fixes.git

#external dataset
$https://nucleisegmentationbenchmark.weebly.com/

Visualize result with Visdom

We now support Visdom for real-time loss visualization during training!

To use Visdom in the browser:

# First install Python server and client 
pip install visdom
# Start the server (probably in a screen or tmux)
python -m visdom.server -env_path runs/visdom/
# http://localhost:8097/

How use

Step 1: Create dataset

#(1) kaggle dataset
./run_createdataset.sh 
#(2) external dataset
./run_createdataset_nuclei.sh

Step 2: Train

./run_train.sh

Step 3: Submission

./run_submission.sh

URLs

• https://towardsdatascience.com/instance-embedding-instance-segmentation-without-proposals-31946a7c53e1
• https://github.com/bodokaiser/piwise/blob/master/piwise/network.py
• https://github.com/lopuhin
• https://github.com/mattmacy/vnet.pytorch/blob/master/train.py
• https://github.com/milesial/Pytorch-UNet/blob/master/myloss.py
• https://github.com/lyakaap
• https://github.com/mdbloice/Augmentor
• https://github.com/kuangliu
• https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/image/image.py
• https://github.com/selimsef/dsb2018_topcoders

Others

• curlwget https://chrome.google.com/webstore/detail/curlwget/jmocjfidanebdlinpbcdkcmgdifblncg?hl=es