This sample implements the 2018 Data Science Bowl challenge. The goal is to segment individual nuclei in microscopy images. The nucleus.py file contains the main parts of the code, and the two Jupyter notebooks

Command line Usage

Train a new model starting from ImageNet weights using train dataset (which is stage1_train minus validation set)

python3 nucleus.py train --dataset=/path/to/dataset --subset=train --weights=imagenet

Train a new model starting from specific weights file using the full stage1_train dataset

python3 nucleus.py train --dataset=/path/to/dataset --subset=stage1_train --weights=/path/to/weights.h5

Resume training a model that you had trained earlier

python3 nucleus.py train --dataset=/path/to/dataset --subset=train --weights=last

Generate submission file from stage1_test images

python3 nucleus.py detect --dataset=/path/to/dataset --subset=stage1_test --weights=<last or /path/to/weights.h5>

Jupyter notebooks

Two Jupyter notebooks are provided as well: inspect_nucleus_data.ipynb and inspect_nucleus_model.ipynb. They explore the dataset, run stats on it, and go through the detection process step by step.