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https://github.com/ammarlodhi255/fine-grained-approach-to-wrist-pathology-recognition
This repository contains the official code for the paper "Learning from the Few: Fine-grained Approach to Wrist Pathology Recognition on a Limited Dataset".
https://github.com/ammarlodhi255/fine-grained-approach-to-wrist-pathology-recognition
explainable-ai fine-grained-classification fracture-detection fracture-recognition gradcam grazpedwri-dx medical-imaging wrist-abnormalities wrist-pathologies wrist-xrays
Last synced: 17 days ago
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This repository contains the official code for the paper "Learning from the Few: Fine-grained Approach to Wrist Pathology Recognition on a Limited Dataset".
- Host: GitHub
- URL: https://github.com/ammarlodhi255/fine-grained-approach-to-wrist-pathology-recognition
- Owner: ammarlodhi255
- License: mit
- Created: 2024-08-23T17:45:00.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-08-24T13:49:00.000Z (5 months ago)
- Last Synced: 2024-11-17T05:19:45.314Z (3 months ago)
- Topics: explainable-ai, fine-grained-classification, fracture-detection, fracture-recognition, gradcam, grazpedwri-dx, medical-imaging, wrist-abnormalities, wrist-pathologies, wrist-xrays
- Language: Python
- Homepage: https://www.sciencedirect.com/science/article/pii/S0010482524011296
- Size: 4.68 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.
--------------------------------------------
### **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** |
--------------------------------------------
### **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](https://www.nature.com/articles/s41597-022-01328-z). The curated dataset can be found at:
* [Curated Dataset](https://figshare.com/s/eb452d2fb36b8ae523c6)
### 1.2. Our Pre-trained Model
The weights of the refined FGVR model can be found at:
* [Weights](https://figshare.com/s/d7c612c26b8a6b51fbe1)
### 1.3. OS
- [x] Windows10
- [x] Ubuntu20.04
- [x] macOS (CPU only)
## 2. Train
- [x] Single GPU Training
- [x] 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](./configs/) 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
- - - - - -
## 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},
}
```
- - - - - -
### Acknowledgment
* Thanks to [PIM](https://github.com/chou141253/FGVC-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.