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### On uncertainty estimation in active learning for image segmentation
<br /> This repository provides the implementation for our paper **On uncertainty estimation in active learning for image segmentation** (Bo Li, Tommy Sonne Alstrøm) [NEED TO ADD A LINK]. We experimentally show that the region based active learning strategy can lead to higher segmentation accuracy and better calibrated model much faster than full image acquisition:
#### Installation and preparation
1. Clone and enter this repo:
```bash
git clone https://lab.compute.dtu.dk/s161488/region_active_learning.git
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cd Region_Active_Learning
```
2. Prepare the Dataset and pertained resnet-50 ckpt
```bash
./requirement.sh
```
3. Create a virtual env with the required packages
```bash
conda env create --act active_learning.yaml
source activate act
```
#### Evaluate the model
In order to evaluate the model at each acquisition step, run
``` python
python3 -c 'import Test as te;te.running_test_for_single_acquisition_step(model_dir)'
Args:
model_dir: the directory that saves the model ckpt
```
#### Train the model
- For full image based active learning, run
```python
python3 -c 'import Train_Active_Full_Im as tafi;tafi.running_loop_active_learning_full_image(stage)'
Args:
stage: int, 0:random, 1:VarRatio, 2:Entropy, 3:BALD
```
- For region based active learning, run
```python
python3 -c 'import Train_Active_Region_Im as tari;tari.running_loop_active_learning_region(stage)'
Args:
stage: int, 0:random, 1:VarRatio, 2:Entropy, 3:BALD
```
#### Reproduce figures
In order to reproduce the figures in the paper, run
```bash