Segformer For Optic Disc Cup Segmentation

🚀 Model Card for Model ID

This SegFormer model is fine - tuned on the REFUGE challenge dataset for expert - level segmentation of optic disc and optic cup in retinal fundus images.

🚀 Quick Start

Use the code below to get started with the model.

import cv2
import torch
import numpy as np

from torch import nn
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 = 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)

✨ Features

This pretrained model enables semantic segmentation of key anatomical structures, namely, the optic disc and optic cup, in retinal fundus images. It takes fundus images as input and outputs the segmentation results.

📚 Documentation

Model Details

Model Description

• Developed by: Xu Sun
• Shared by: Xu Sun
• Model type: Image segmentation
• License: Apache - 2.0

Uses

This pretrained model enables semantic segmentation of key anatomical structures, namely, the optic disc and optic cup, in retinal fundus images. It takes fundus images as input and outputs the segmentation results.

Bias, Risks, and 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.

📄 License

This model is licensed under Apache - 2.0.

📚 Citation [optional]

BibTeX

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APA

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📞 Model Card Contact

• pamixsun@gmail.com