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SAM-Swin: SAM-Driven Dual-Swin Transformers with Adaptive Lesion Enhancement for Laryngo-Pharyngeal Tumor Detection

This repo is the official implementation of SAM-Swin: SAM-Driven Dual-Swin Transformers with Adaptive Lesion Enhancement for Laryngo-Pharyngeal Tumor Detection.

Introduction

The SAM-Swin mainly consists of four pivotal components: SAM2-guided lesion location (SAM2-GLLM), whole image branch (WIB), lesion region branch (LRB), and multi-scale lesion-aware enhancement module (MS-LEAM).

• By leveraging the advanced object segmentation capabilities of the SAM2, we pioneerly integrate SAM2 into the SAM-Swin framework, enabling SAM-Swin to achieve highly precise segmentation of the lesion region.
• We propose MS-LAEM designed to adaptively enhance the learning of nuanced complementary features across various scales, improving the quality of feature extraction and representation.
• We introduce the multi-scale CAG loss, a novel approach that employs targeted supervision to facilitate the extraction of class-specific features within the model.

Fine-tune SAM2

To fine-tune SAM2 tailored for your tasks, we recommend following the guidelines provided in the original repository: MedSAM2

Dataset

Organize your datasets in the following manner:

datasets/
├── dataset1/
│   ├── global/
│   │   ├── train/
│   │   │   ├── benign/
│   │   │   ├── normal/
│   │   │   └── tumor/
│   │   ├── val/
│   │   │   ├── benign/
│   │   │   ├── normal/
│   │   │   └── tumor/
│   │   └── test/
│   │       ├── benign/
│   │       ├── normal/
│   │       └── tumor/
│   └── local_seg/
│       ├── train/
│       │   ├── benign/
│       │   ├── normal/
│       │   └── tumor/
│       ├── val/
│       │   ├── benign/
│       │   ├── normal/
│       │   └── tumor/
│       └── test/
│           ├── benign/
│           ├── normal/
│           └── tumor/
├── dataset6/
│   └── ...

Training

We train the SAM-Swin in two stages.

1. Stage 1, run:

   python -m torch.distributed.launch --nproc_per_node 2 --master_port 12345 main.py --cfg configs/dynamic.yaml --batch-size 32 --pretrained swinv2_base_patch4_window16_256.pth --cache-mode full --amp-opt-level O1 --accumulation-steps 4 --fused_window_process --fused_layernorm --tag exp
   

2. Stage 2, run:

   python -m torch.distributed.launch --nproc_per_node 2 --master_port 12345 main.py --cfg configs/ft_baseline.yaml --batch-size 32 --pretrained <Your path of latest checkpoint at the Stage 1> --cache-mode full --amp-opt-level O1 --accumulation-steps 4 --fused_window_process --fused_layernorm --tag exp_ft
   

Testing

Using DDP, Run:

python -m torch.distributed.launch --nproc_per_node 2 --master_port 12345 main.py --cfg configs/dynamic.yaml --resume <Your path of the checkpoint> --cache-mode full --amp-opt-level O1 --accumulation-steps 4 --fused_window_process --fused_layernorm --tag exp --eval

Acknowledgement

The code of SAM-Swin is built upon MedSAM2 and Swin Transformer, and we express our gratitude to these awesome projects.

Citing SAM-Swin

@misc{wei2024samswinsamdrivendualswintransformers,
      title={SAM-Swin: SAM-Driven Dual-Swin Transformers with Adaptive Lesion Enhancement for Laryngo-Pharyngeal Tumor Detection}, 
      author={Jia Wei and Yun Li and Xiaomao Fan and Wenjun Ma and Meiyu Qiu and Hongyu Chen and Wenbin Lei},
      year={2024},
      eprint={2410.21813},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2410.21813}, 
}