CaraNet: Context Axial Reverse Attention Network for Small Medical Objects Segmentation

The technique report is here: CaraNet

Architecture of CaraNet

Backbone

We use Res2Net as our backbone.

Context module

We choose our CFP module as context module, and choose the dilation rate is 8. For the details of CFP module you can find here: CFPNet. The architecture of CFP module as shown in following figure:

Axial Reverse Attention

As shown in architecture of CaraNet, the Axial Reverse Attention (A-RA) module contains two routes: 1) Reverse attention; 2) Axial-attention.

Installation & Usage

Enviroment

• Enviroment: Python 3.6;
• Install some packages:

conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch

conda install opencv-python pillow numpy matplotlib

• Clone this repository

git clone https://github.com/AngeLouCN/CaraNet

Training

• Download the training and texting dataset from this link: Experiment Dataset
• Change the --train_path & --test_path in Train.py
• Run Train.py
• Testing dataset is ordered as follow:

|-- TestDataset
|   |-- CVC-300
|   |   |-- images
|   |   |-- masks
|   |-- CVC-ClinicDB
|   |   |-- images
|   |   |-- masks
|   |-- CVC-ColonDB
|   |   |-- images
|   |   |-- masks
|   |-- ETIS-LaribPolypDB
|   |   |-- images
|   |   |-- masks
|   |-- Kvasir
|       |-- images
|       |-- masks

Testing

• Change the data_path in Test.py

Evaluation

• Change the image_root and gt_root in eval_Kvasir.py
• You can also run the matlab code in eval fold, it contains other four measurement metrics results.
• You can download the segmentation maps of CaraNte from this link: CaraNet

Segmentation Results

• Polyp Segmentation Results

• Small polyp analysis

The x-axis is the proportion size (%) of polyp; y-axis is the average mean dice coefficient.

Kvasir	CVC-ClinicDB	CVC-ColonDB	ETIS	CVC-300

• Brain Tumor Segmentation Results

• Small tumor analysis

Citation

@article{lou2021cfpnet,
  title={CFPNet: Channel-wise Feature Pyramid for Real-Time Semantic Segmentation},
  author={Lou, Ange and Loew, Murray},
  journal={arXiv preprint arXiv:2103.12212},
  year={2021}
}