GIVTED-Net

The architecture of GIVTED-Net.

This is the official repository for "GIVTED-Net: GhostNet-Mobile Involution ViT Encoder-Decoder Network for Lightweight Medical Image Segmentation.". Here, we introduce MIViT (Mobile Involution Vision Transformer) modules powered by newly formed transformer blocks, InvoFormer. The blocks follow the design principle of MetaFormer with an involution-based token mixer and a squeeze-and-excitation module-based channel MLP. These components, along with the Ghost bottlenecks of GhostNet, are compiled in the proposed medical image segmentation model, GIVTED-Net. Ghost bottlenecks and MIViT modules are employed in the encoder and decoder, respectively. The model is lightweight (0.19M of parameters and 0.56GFLOPs), fast (2.47 FPS on the Raspberry Pi 4), and quantitatively superior to prior models.

Requirements

This implementation requires some libraries and frameworks. They can easily be installed by:

pip install -r requirements.txt

Dataset

We use the KvasirInstrument, ISIC2018, and WBCImage datasets to assess the performance of the model. We arrange the datasets in the following:

.
└── experiment/
    ├── KvasirInstrument/
    │   ├── TrainDataset/
    │   │   ├── images/
    │   │   │   ├── image_00.jpg
    │   │   │   └── ...
    │   │   └── masks/
    │   │       ├── image_00.jpg
    │   │       └── ...
    │   ├── TestDataset/
    │   │   ├── images/
    │   │   │   ├── image_00.jpg
    │   │   │   └── ...
    │   │   └── masks/
    │   │       ├── image_00.jpg
    │   │       └── ...
    │   ├── result/
    │   │   ├── image_00.jpg
    │   │   └── ...
    │   └── model_pth/
    │       ├── GIVTEDNet.pth
    │       └── ...
    ├── ISIC2018/
    │   ├── TrainDataset/
    │   │   ├── images/
    │   │   │   ├── image_00.jpg
    │   │   │   └── ...
    │   │   └── masks/
    │   │       ├── image_00.jpg
    │   │       └── ...
    │   ├── TestDataset/
    │   │   ├── images/
    │   │   │   ├── image_00.jpg
    │   │   │   └── ...
    │   │   └── masks/
    │   │       ├── image_00.jpg
    │   │       └── ...
    │   ├── result/
    │   │   ├── image_00.jpg
    │   │   └── ...
    │   └── model_pth/
    │       ├── GIVTEDNet.pth
    │       └── ...
    └── WBCImage/
        ├── TrainDataset/
        │   ├── images/
        │   │   ├── image_00.jpg
        │   │   └── ...
        │   └── masks/
        │       ├── image_00.jpg
        │       └── ...
        ├── TestDataset/
        │   ├── images/
        │   │   ├── image_00.jpg
        │   │   └── ...
        │   └── masks/
        │       ├── image_00.jpg
        │       └── ...
        ├── result/
        │   ├── image_00.jpg
        │   └── ...
        └── model_pth/
            ├── GIVTEDNet.pth
            └── ...

Training

To train our model:

# Please choose either one: KvasirInstrument, ISIC2018, WBCImage
python train.py --dataset_name "KvasirInstrument" 

Testing

We conducted the evaluation on the Raspberry Pi 4. However, it can be run on some other hardware, too.

python eval.py

Benchmark

Segmentation Performance

The qualitative results of GIVTED-Net and other models. For quantitative results, please check our paper.

Computational Performance

Model	Parameters	FLOPs	FPS
TransUNet	105.28M	50.70G	0.22
U-Net	31.04M	83.86G	0.14
LeViT-UNet-384	52.15M	50.82G	0.26
LeViT-UNet-192	19.89M	38.58G	0.36
LeViT-UNet-128s	15.89M	34.86G	0.39
ResUNet++	4.06M	24.28G	0.31
SA-UNet	0.54M	4.86G	1.48
Mobile-PolypNet	0.23M	2.24G	0.76
NanoNet-A	0.29M	2.44G	1.66
NanoNet-B	0.18M	1.97G	1.71
NanoNet-C	43.34K	0.44G	3.67
GIVTED-Net (Ours)	0.19M	0.56G	2.47

Citation

@ARTICLE{10552270,
  author={Dwika Hefni Al-Fahsi, Resha and Naghim Fauzaini Prawirosoenoto, Ahmad and Adi Nugroho, Hanung and Ardiyanto, Igi},
  journal={IEEE Access}, 
  title={GIVTED-Net: GhostNet-Mobile Involution ViT Encoder-Decoder Network for Lightweight Medical Image Segmentation}, 
  year={2024},
  volume={12},
  number={},
  pages={81281-81292},
  keywords={Image segmentation;Biomedical imaging;Transformers;Convolutional neural networks;Computational modeling;Decoding;Convolutional neural networks;Deep learning;Deep learning;GhostNet;lightweight model;medical image segmentation;mobile involution ViT},
  doi={10.1109/ACCESS.2024.3411870}
}

Reference

• KvasirInstrument
• ISIC2018
• WBCImage