Learning from the Few: Fine-grained Approach to Pediatric Wrist Pathology Recognition on a Limited Dataset

Paper URL: https://www.sciencedirect.com/science/article/pii/S0010482524011296

We approach wrist pathology recognition as a fine-grained recognition problem. We refine our fine-grained architecture through ablation analysis and the integration of LION optimization. Leveraging Grad-CAM, an explainable AI technique, we highlight these regions. Despite using limited data, reflective of real-world medical study constraints, our method consistently outperforms state-of-the-art image recognition models on both augmented and original (challenging) test sets. Our proposed refined architecture achieves an increase in accuracy of 1.06% and 1.25% compared to the baseline method, resulting in accuracies of 86% and 84%, respectively. Moreover, our approach demonstrates the highest fracture sensitivity of 97%, highlighting its potential to enhance wrist pathology recognition.

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Evaluation Against Other Deep Neural Networks

Model	Test Accuracy (%)
EfficientNetV2	53.59
NFNet	65.40
VGG16	65.82
ViT	70.25
DeiT3	70.89
RegNet	72.36
DenseNet201	73.42
MobileNetV2	76.37
CMAL	76.58
RexNet100	77.43
ResNet101	77.43
IELT	78.10
DenseNet121	78.21
ResNest101e	78.27
InceptionV4	78.69
MetaFormer	78.90
ResNet50	79.11
InceptionV3	79.54
EfficientNet_b0	79.96
YOLOv8x	80.50
HERBS	82.70
Our Approach (PIM for FGVR)	84.38

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LION Integration and FPN Adjustment

Model	Test Set 1 Accuracy (%)	Test Set 2 Accuracy (%)
PIM	84.38	82.50
PIM + LION	85.44	83.75

1. Environment setting

1.0. Package

• Install Requirements

pip install -r requirements.txt

1.1. Dataset

In this paper, we use a dataset curated from GRAZPEDWRI-DX. The curated dataset can be found at:

• Curated Dataset

1.2. Our Pre-trained Model

The weights of the refined FGVR model can be found at:

• Weights

1.3. OS

• Windows10
• Ubuntu20.04
• macOS (CPU only)

2. Train

• Single GPU Training
• DataParallel (single machine multi-gpus)
• DistributedDataParallel

(more information: https://pytorch.org/tutorials/intermediate/ddp_tutorial.html)

2.1. Data

train, val, and test data structures:

├── train/
│   ├── 0/
│   |   ├── 0/0133_0306769778_07_WRI-R2_M015-1.png
│   |   ├── 0133_0306769778_07_WRI-R2_M015-3.png
│   |   └── ....
│   ├── 1/
│   |   ├── 0025_0483842914_01_WRI-L2_F000.png
│   |   ├── 0053_1119833109_03_WRI-R1_F005.png
│   |   └── ....
│   └── ....
└── val/
│   ├── 0/
│   |   ├── 0133_0306769778_07_WRI-R2_M015-0.png
│   |   ├── 0133_0306769778_07_WRI-R2_M015-2.png
│   |   └── ....
│   ├── 1/
│   |   ├── 0042_0827512771_04_WRI-R2_M015.png
│   |   ├── 0071_0680563744_02_WRI-R1_F009.png
│   |   └── ....
│   └── ....
└── test/
│   ├── 0/
│   |   ├── 0772_0547017117_03_WRI-R1_M017-0.png
│   |   ├── 0772_0547017117_03_WRI-R1_M017-1.png
│   |   └── ....
│   ├── 1/
│   |   ├── 0069_0502540283_01_WRI-L1_M013.png
│   |   ├── 0078_1212376595_01_WRI-L1_M011.png
│   |   └── ....
│   └── ....
└── test2/
│   ├── 0/
│   |   ├── 0772_0547017117_03_WRI-R1_M017.png
│   |   ├── 0834_0240036198_01_WRI-R1_M014.png
│   |   └── ....
│   ├── 1/
│   |   ├── 0069_0502540283_01_WRI-L1_M013.png
│   |   ├── 0115_0432451427_01_WRI-L2_M004.png
│   |   └── ....
│   └── ....

2.2. Configuration

All the important configuration files are located at config folder. You can directly modify yaml file if needed.

2.3. Run

python main.py --c ./configs/wrist.yaml

model will save in ./records/{project_name}/{exp_name}/backup/

2.5. Multi-GPUs

comment out main.py line 66

model = torch.nn.DataParallel(model, device_ids=None)

3. Evaluation

If you want to evaluate our pre-trained model or your custom-trained model (specify the path to your model in the config file):

python main.py --c ./configs/eval.yaml

Results will be saved in ./records/{project_name}/{exp_name}/eval_results.txt

4. Heatmap Generation

python heat.py --c ./configs/wrist.yaml --img data/test/1/0069_0502540283_01_WRI-L1_M013.png --save_img ./0069_0502540283_01_WRI-L1_M013.png

5. Inference

If you want to reason your picture and get the confusion matrix, please provide configs/eval.yaml

python infer.py --c ./configs/eval.yaml

Results will be saved in ./records/{project_name}/{exp_name}/infer_results.txt

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Citation

If you find our paper useful in your research, please consider citing:

    @article{AHMED2024Learning,
        title = {Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset},
        author = {Ammar Ahmed and Ali Shariq Imran and Zenun Kastrati and Sher Muhammad Daudpota and Mohib Ullah and Waheed Noor},
        journal = {Computers in Biology and Medicine},
        volume = {181},
        pages = {109044},
        year = {2024},
        issn = {0010-4825},
        doi = {https://doi.org/10.1016/j.compbiomed.2024.109044},
        url = {https://www.sciencedirect.com/science/article/pii/S0010482524011296},
}

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Acknowledgment

• Thanks to PIM for their base Pytorch implementation of Plug-in Module.

• This work was supported in part by the Department of Computer Science (IDI), Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), Gjøvik, Norway; and in part by the Curricula Development and Capacity Building in Applied Computer Science for Pakistani Higher Education Institutions (CONNECT), Project number: NORPART-2021/10502, funded by DIKU.