|
--- |
|
license: apache-2.0 |
|
tags: |
|
- image-segmentation |
|
- vision |
|
- fundus |
|
- optic disc |
|
- optic cup |
|
widget: |
|
- src: >- |
|
https://huggingface.co/pamixsun/swinv2_tiny_for_glaucoma_classification/resolve/main/example.jpg |
|
example_title: fundus image |
|
--- |
|
# Model Card for Model ID |
|
|
|
<!-- Provide a quick summary of what the model is/does. --> |
|
|
|
This model utilizes a Swin Transformer architecture and has undergone supervised fine-tuning on retinal fundus images from the [REFUGE challenge dataset](https://refuge.grand-challenge.org/). |
|
It is specialized in automated analysis of retinal fundus photographs for glaucoma detection. |
|
By extracting hierarchical visual features from input fundus images in a cross-scale manner, the model is able to effectively categorize each image as either glaucoma or non-glaucoma. Extensive experiments demonstrate that this model architecture achieves state-of-the-art performance on the REFUGE benchmark for fundus image-based glaucoma classification. |
|
To obtain optimal predictions, it is recommended to provide this model with standardized retinal fundus photographs captured using typical fundus imaging protocols. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
|
|
<!-- Provide a longer summary of what this model is. --> |
|
|
|
- **Developed by:** [Xu Sun](https://pamixsun.github.io) |
|
- **Shared by:** [Xu Sun](https://pamixsun.github.io) |
|
- **Model type:** Image classification |
|
- **License:** Apache-2.0 |
|
|
|
## Uses |
|
|
|
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
|
|
|
The pretrained model provides glaucoma classification functionality solely based on analysis of retinal fundus images. |
|
You may directly utilize the raw model without modification to categorize fundus images as either glaucoma or non-glaucoma. |
|
This model is specialized in extracting discriminative features from fundus images to identify glaucoma manifestations. |
|
However, to achieve optimal performance, it is highly recommended to fine-tune the model on a representative fundus image dataset prior to deployment in real-world applications. |
|
|
|
## Bias, Risks, and Limitations |
|
|
|
<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
|
|
|
The model has undergone specialized training and fine-tuning exclusively using retinal fundus images, |
|
with the objective to perform semantic segmentation of anatomical structures including the optic disc and optic cup. |
|
Therefore, in order to derive optimal segmentation performance, it is imperative to ensure that only fundus images are entered as inputs to this model. |
|
|
|
## How to Get Started with the Model |
|
|
|
Use the code below to get started with the model. |
|
|
|
```python |
|
import cv2 |
|
import torch |
|
|
|
from transformers import AutoImageProcessor, SegformerForSemanticSegmentation |
|
|
|
image = cv2.imread('./example.jpg') |
|
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
|
|
|
processor = AutoImageProcessor.from_pretrained("pamixsun/segformer_for_optic_disc_cup_segmentation") |
|
model = SegformerForSemanticSegmentation.from_pretrained("pamixsun/segformer_for_optic_disc_cup_segmentation") |
|
|
|
inputs = processor(image, return_tensors="pt") |
|
|
|
with torch.no_grad(): |
|
outputs = self.seg_model(**inputs) |
|
logits = outputs.logits.cpu() |
|
|
|
upsampled_logits = nn.functional.interpolate( |
|
logits, |
|
size=image.shape[:2], |
|
mode="bilinear", |
|
align_corners=False, |
|
) |
|
|
|
pred_disc_cup = upsampled_logits.argmax(dim=1)[0].numpy().astype(np.uint8) |
|
|
|
``` |
|
|
|
## Citation [optional] |
|
|
|
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
|
|
|
**BibTeX:** |
|
|
|
[More Information Needed] |
|
|
|
**APA:** |
|
|
|
[More Information Needed] |
|
|
|
|
|
## Model Card Contact |
|
|
|
- pamixsun@gmail.com |
