This repository is an official implementation of the paper [A Spatial-Temporal Deformable Attention based Framework for Breast Lesion Detection in Videos]. (MICCAI-2023)

Detecting breast lesion in videos is crucial for computer- aided diagnosis. Existing video-based breast lesion detection approaches typically perform temporal feature aggregation of deep backbone fea- tures based on the self-attention operation. We argue that such a strat- egy struggles to effectively perform deep feature aggregation and ig- nores the useful local information. To tackle these issues, we propose a spatial-temporal deformable attention based framework, named STNet. Our STNet introduces a spatial-temporal deformable attention module to perform local spatial-temporal feature fusion. The spatial-temporal deformable attention module enables deep feature aggregation in each stage of both encoder and decoder. To further accelerate the detection speed, we introduce an encoder feature shuffle strategy for multi-frame prediction during inference. In our encoder feature shuffle strategy, we share the backbone and encoder features, and shuffle encoder features for decoder to generate the predictions of multiple frames. The exper- iments on the public breast lesion ultrasound video dataset show that our STNet obtains a state-of-the-art detection performance, while oper- ating twice as fast inference speed.

• Linux, CUDA>=9.2, GCC>=5.4

• Python>=3.7

  We recommend you to use Anaconda to create a conda environment:

  conda create -n STNet python=3.7 pip

  Then, activate the environment:

  conda activate STNet

• PyTorch>=1.5.1, torchvision>=0.6.1 (following instructions here)

  For example, if your CUDA version is 9.2, you could install pytorch and torchvision as following:

  conda install pytorch=1.5.1 torchvision=0.6.1 cudatoolkit=9.2 -c pytorch

• Other requirements

  pip install -r requirements.txt

cd ./models/ops
sh ./make.sh
# unit test (should see all checking is True)
python test.py

The dataset is provided by CVA-Net, which is available for only non-commercial use in research or educational purpose. As long as you use the database for these purposes, you can edit or process images and annotations in this database. Please contact the authors of CVA-Net for getting the access to the dataset.

code_root/
└── miccai_buv/
      ├── rawframes/
      ├── train.json
      └── val.json

For example, the command for training CVA-NET on 8 GPUs is as following:

GPUS_PER_NODE=8 ./tools/run_dist_launch.sh 8 ./configs/configs.sh

If you are using slurm cluster, you can simply run the following command to train on 1 node with 8 GPUs:

GPUS_PER_NODE=8 ./tools/run_dist_slurm.sh <partition> CVA-Net 8 configs/configs.sh

We provide a trained model on the validation set. You can download it from here and put it in ./checkpoints/. Then you can test it by running the following command:

GPUS_PER_NODE=1 ./tools/run_dist_launch.sh 1 ./configs/test.sh 

The code of this repository is built on https://github.com/jhl-Det/CVA-Net. We thank the authors of CVA-Net for their great work.