Editing Ai Ophthalmology (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Diabetic retinopathy grading from fundus images:'''
<syntaxhighlight lang="python">
import torch
import torch.nn as nn
from torchvision import models, transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
import pandas as pd
import numpy as np
from sklearn.metrics import cohen_kappa_score, roc_auc_score

class RetinalDataset(Dataset):
    """Retinal fundus image dataset for DR grading."""
    DR_GRADES = {0: 'No DR', 1: 'Mild', 2: 'Moderate', 3: 'Severe', 4: 'Proliferative'}
    def __init__(self, df, transform=None):
        self.df = df
        self.transform = transform

    def __len__(self): return len(self.df)
    def __getitem__(self, idx):
        row = self.df.iloc[idx]
        img = Image.open(row['image_path']).convert('RGB')
        if self.transform: img = self.transform(img)
        return img, int(row['dr_grade'])

# EfficientNet-B4 for DR grading (strong baseline)
transform = transforms.Compose([
    transforms.Resize(380), transforms.CenterCrop(380),
    transforms.RandomHorizontalFlip(), transforms.RandomVerticalFlip(),
    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

model = models.efficientnet_b4(weights='IMAGENET1K_V1')
model.classifier[1] = nn.Linear(model.classifier[1].in_features, 5)  # 5 DR grades

# Ordinal classification loss (preserves grade ordering)
class OrdinalLoss(nn.Module):
    """Convert ordinal regression to K-1 binary classifiers."""
    def __init__(self, n_classes=5):
        super().__init__()
        self.n_classes = n_classes
    def forward(self, logits, targets):
        cumulative_targets = torch.zeros(len(targets), self.n_classes - 1)
        for i, t in enumerate(targets):
            cumulative_targets[i, :t] = 1  # 1 for all grades ≤ target
        return nn.BCEWithLogitsLoss()(logits[:, :-1], cumulative_targets.to(logits.device))

criterion = OrdinalLoss(n_classes=5)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-2)

# Key metric: quadratic weighted kappa (Kaggle DR competition metric)
def quadratic_kappa(y_true, y_pred):
    return cohen_kappa_score(y_true, y_pred, weights='quadratic')
</syntaxhighlight>

; Ophthalmology AI tools
: '''Commercial DR screening''' → IDx-DR (Digital Diagnostics), Eyenuk EyeArt, Retinalyze
: '''Multi-disease screening''' → DeepMind Streams (now Verily), Notal Vision
: '''AMD/OCT analysis''' → Heidelberg HEYEX AI, Zeiss CIRRUS AI
: '''Glaucoma''' → Glaukos AI, Ora ClinicalEdge, Optomed Aurora
: '''Datasets''' → EyePACS (1M+ images), Kaggle DR, DRIVE, ORIGA, REFUGE
</div>

<div style="background-color: #8B4500; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">