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SSAD: Self-supervised Auxiliary Detection Framework for Panoramic X-ray based Dental Disease Diagnosis

This is the offical implement code of artical https://arxiv.org/abs/2406.13963. It employs the reconstruction-based Self-supervised methods to assist deep-learning model in diagnosing the Dental Disease, and the whole framework as below:

Requirements

# STEP1: Find a PyTorch version that matches your CUDA version from the official PyTorch website, for example:
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113

# STEP2: Install Requirements
pip install -r requirements.txt

# STEP3 Install CLIP
cd models/CLIP && pip install .

# STEP4 Refer to DINO for guidance on installing MultiScaleDeformableAttention.

DINO website is there

Train

Data Preparation: Please refer at there

## Train SSAD with detectron2, For Example:
# yaml
python train_diffdet.py --num-gpus 1 --config-file configs/faster_rcnn/diffdet.dentex.swinbase.disease.yaml OUTPUT_DIR "the path of model weights" MODEL.WEIGHTS "checkpoints/swin_base_patch4_window7_224_22k.pth"

# py
python train_lazyconfig_net.py --num-gpus 1 --config-file configs/faster_rcnn/faster_rcnn_vitB_vitdet.py "train.output_dir='the path of model weights'" "train.init_checkpoint='checkpoints/vitdet.pkl'" 

## Train Yolov8, For Example:
cd yolov8-ssad
# Run Baseline
export DEFAULT_CFG_PATH=./hyp.baseline.yaml && python train.py
# Run SSAD
export DEFAULT_CFG_PATH=./hyp.ssad.yaml && python train_ssad.py

Eval

Model Zoo