Annotation Efforts in Image Segmentation can be Reduced by Neural Network Bootstrapping

This repository is the implementation of the BMT2022 Contribution Annotation Efforts in Image Segmentation can be Reduced by Neural Network Bootstrapping.

Project Structure

Overview of this repository:

.
├── DLIP
│   ├── data #  Contains the defined datasets as PyTorch Lightning DataModules & Datasets.   
│   ├── experiments #  Contains experiment configurations as yaml files.
│   ├── models #  Contains the defined models as PyTorch Modules.    
│   ├── objectives #  Contains the defined objectives as PyTorch Modules.
│   ├── scripts #  Contains the training and inference scripts.
│   └── utils #  Contains utils functions, which can be used by all modules.

The training (DLIP/scripts/train.py) script is configured by the defined experiments (DLIP/experiments) and utilize the defined datamodules (DLIP/data), models (DLIP/models) and objectives (DLIP/objectives).

Install

Prerequisite

• Python == 3.8.5
• Pip == 21.2.4

Conda Environment

conda create --name YOUR_ENV_NAME python=3.8.5

Pip Installation

1. Run pip install -e .