Goal of this project was to create an algorithm to automate nucleus detection from biomedical images. By automating nucleus detection, we could help unlock cures faster—from rare disorders to the common cold.
For this competition, I modified Matterport's implementation of Mask-RCNN deep neural network for object instance segmentation. I adapted the existing model configurations to detect small nuclei in images with varying size and modality. To ensure that the model doesn't overfit, I used image augmentation on all the train dataset.
mrcnnfolder contain the model files and configuration from Mask-RCNN.main.pyhas wrapped up everything from training to detection.
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=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>