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https://github.com/tejacherukuri/guided-context-gating

This is the official repository for paper titled 'Guided Context Gating: Learning to Leverage Salient Lesions in Retinal Fundus Images'
https://github.com/tejacherukuri/guided-context-gating

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This is the official repository for paper titled 'Guided Context Gating: Learning to Leverage Salient Lesions in Retinal Fundus Images'

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README 

        
# Guided Context Gating: Learning to Leverage Salient Lesions in Retinal Fundus Images



_An attention mechanism designed to suit the identification of micro and macro lesions in retinal fundus images for accurate diabetic retinopathy classification._



This paper has been accepted at IEEE International Conference on Image Processing (ICIP), 2024.



[[IEEE Xplore]](https://ieeexplore.ieee.org/document/10647604)[[ArXiv]](https://arxiv.org/abs/2406.13126)[[Demo]](https://huggingface.co/spaces/tejacherukuri/retinal-lesion-detector)[[Dataset]](https://zenodo.org/records/4647952)



## Table of Contents

1. [Motivation](#motivation)

2. [Architecture](#architecture)

3. [Quantitative Evaluation](#quantitative-evaluation)

4. [Qualitative Evaluation](#qualitative-evaluation)

5. [Usage](#usage)

   - [Setup](#setup)

   - [Inference on Custom Samples](#inference-on-custom-samples)

   - [Inference through Huggingface Spaces](#inference-through-huggingface-spaces)

6. [Citation](#citation)



## Motivation

Diabetic Retinopathy (DR) grading is a highly challenging task due to the complexity of lesion variability and their contribution to different severity grades. Lesions such as Microaneurysms, Exudates, Hemorrhages, and Cotton Wool Spots vary significantly in size, shape, and appearance, often misleading the classification process. **Some lesions are barely visible, while others are prominent, yet every minor lesion plays a critical role in determining the correct severity grade.** Misclassifying even a single lesion can lead to incorrect grading, as the distinction between severity grades is often subtle and highly dependent on lesion characteristics.



## Architecture

Our approach consists of three key sub-blocks:

1. **Context Formulation**: Captures the global spatial context of the image.

2. **Channel Correlation**: Identifies channel-wise relationships to enhance feature relevance.

3. **Guided Gating**: Uses the global context as a guiding signal to zero in on localized lesions.



![Architecture Diagram](architecture.jpg)



## Quantitative Evaluation

| Class | Acc   | Prec  | Rec   | F1    | AUC    | Support |

|-------|-------|-------|-------|-------|--------|-------|

| No DR | 95.65 | 91.67 | 95.65 | 93.62 | 99.43  | 23    |

| Mild NPDR | 100.0 | 100.0 | 100.0 | 100.0 | 100.0  | 01    |

| Moderate NPDR | 75.00 | 80.00 | 75.00 | 77.42 | 92.97  | 16    |

| Severe NPDR | 100.0 | 97.37 | 100.0 | 98.67 | 100    | 37    |

| Very Severe NPDR | 55.56 | 66.67 | 55.56 | 60.61 | 92.68  | 18    |

| PDR | 97.14 | 94.44 | 97.14 | 95.77 | 99.56  | 35    |

| Advanced PDR | 95.45 | 91.3  | 95.45 | 93.33 | 99.09  | 22    |

| Macro | 90.13 | 88.78 | 88.4  | 88.49 | 97.80  | 152   |

| Weighted | -   | 89.51 | 90.13 | 89.73 | 98.64  | -     |



## Qualitative Evaluation

![Attention Maps](qualitative_result.png)



## Usage

### Setup

1. Installing the project:

```bash

pip install git+https://github.com/TejaCherukuri/Retinal-Lesion-Detector.git

```

2. Creating a Virtual Environment:

   - The command `python -m venv gcg_venv` works the same on **Windows**, **macOS**, and **Linux**.



3. Activating the Virtual Environment:

   - On **macOS/Linux**, use `source gcg_venv/bin/activate`.

   - On **Windows**, use `gcg_venv\Scripts\activate`.



4. Install all the necessary dependencies:

```bash

pip install -r requirements.txt

```

### Inference on Custom Samples



1. Prepare a list of test image paths (relative to your working directory).

2. Pass the list of paths to the `predict()` method from the inference pipeline.

3. The method will return predictions and generate visual outputs.



```python

from gcg.pipelines import predict



# Example test image paths

image_paths = [

    'test_images/image_1.jpg',

    'test_images/image_2.jpg'

]



# Get predictions

predictions_list = predict(image_paths)

print(predictions_list)

```

Outputs



- **Predictions**: The predicted classes for the provided images.

- **Heatmaps and Superimposed Images**:

  - Generated for each test image.

  - Saved to the `heatmaps` directory in your working directory.

    

Example heatmap:

  

![heatmap](heatmaps/heatmap_198_Moderate_NPDR.jpg)



### Inference through Huggingface Spaces



For those of you who prefer user interface over code, access the link to huggingface spaces [[Demo]](https://huggingface.co/spaces/tejacherukuri/Guided-Context-Gating)



### Citation

If you find GCG useful for your research and applications, please cite using this BibTeX:

```bibtex

@article{cherukuri2024guided,

  title={Guided Context Gating: Learning to leverage salient lesions in retinal fundus images},

  author={Cherukuri, Teja Krishna and Shaik, Nagur Shareef and Ye, Dong Hye},

  journal={arXiv preprint arXiv:2406.13126},

  year={2024}

}

```