Editing Radiology AI (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Chest X-ray classification pipeline:'''
<syntaxhighlight lang="python">
import torch
import torch.nn as nn
from torchvision import models, transforms
from torch.utils.data import Dataset, DataLoader
import pydicom
import numpy as np
from PIL import Image

class ChestXRayDataset(Dataset):
    """Load CheXpert or CheXpert-like chest X-ray dataset."""
    def __init__(self, df, transform=None):
        self.df = df
        self.transform = transform
        # 14 pathology labels
        self.labels = ['No Finding', 'Enlarged Cardiomediastinum', 'Cardiomegaly',
                       'Lung Opacity', 'Lung Lesion', 'Edema', 'Consolidation',
                       'Pneumonia', 'Atelectasis', 'Pneumothorax', 'Pleural Effusion',
                       'Pleural Other', 'Fracture', 'Support Devices']

    def __len__(self): return len(self.df)
    def __getitem__(self, idx):
        row = self.df.iloc[idx]
        img = Image.open(row['Path']).convert('RGB')
        if self.transform: img = self.transform(img)
        labels = torch.tensor([row.get(l, 0) for l in self.labels], dtype=torch.float)
        return img, labels

# DenseNet-121 (CheXNet architecture)
transform = transforms.Compose([
    transforms.Resize(256), transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
model = models.densenet121(weights='IMAGENET1K_V1')
model.classifier = nn.Linear(1024, 14)  # 14 pathologies, multi-label

# Multi-label binary cross-entropy with label smoothing
criterion = nn.BCEWithLogitsLoss()

# Clinical note: output sigmoid probabilities + threshold per pathology
# Different thresholds for different pathologies based on clinical priority:
# Pneumothorax: sensitivity priority (low threshold ~0.2)
# Cardiomegaly: specificity priority (high threshold ~0.6)
THRESHOLDS = {'Pneumothorax': 0.2, 'Pneumonia': 0.3, 'default': 0.5}

def predict_with_clinical_thresholds(logits, labels):
    probs = torch.sigmoid(logits)
    preds = torch.zeros_like(probs)
    for i, label in enumerate(labels):
        thresh = THRESHOLDS.get(label, THRESHOLDS['default'])
        preds[:, i] = (probs[:, i] > thresh).float()
    return probs, preds
</syntaxhighlight>

; Radiology AI products and datasets
: '''Chest X-ray detection''' → CheXNet (research), Enlitic, Annalise CXR, Qure.ai qXR
: '''CT triage''' → Viz.ai (PE, stroke), Aidoc (hemorrhage, PE), Accipiolite
: '''Mammography''' → iCAD PowerLook, Hologic Genius AI, Transpara (Screenpoint)
: '''Bone X-ray''' → Gleamer BoneView, Nanox.AI, Zebra Medical
: '''Datasets''' → CheXpert (224K), MIMIC-CXR (227K), RSNA PE, NIH ChestX-ray14
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