AI in Radiology

Chest X-Ray Interpretation

import torchvision.models as models

class ChestXRayClassifier(nn.Module):
    def __init__(self, n_findings=14):
        super().__init__()
        self.backbone = models.densenet121(pretrained=True)
        self.backbone.classifier = nn.Linear(1024, n_findings)

    def forward(self, x):
        return self.backbone(x)

FINDINGS = ['Atelectasis', 'Cardiomegaly', 'Effusion', 'Infiltration',
            'Mass', 'Nodule', 'Pneumonia', 'Pneumothorax',
            'Consolidation', 'Edema', 'Emphysema', 'Fibrosis',
            'Pleural_Thickening', 'Hernia']

CT Scan Analysis

3D Volume Processing

class LungNoduleDetector(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Conv3d(1, 32, 3, padding=1), nn.BatchNorm3d(32), nn.ReLU(),
            nn.Conv3d(32, 64, 3, padding=1), nn.BatchNorm3d(64), nn.ReLU(),
            nn.MaxPool3d(2))
        self.proposal_net = nn.Conv3d(64, 12, 1)

    def forward(self, volume):
        features = self.encoder(volume)
        return self.proposal_net(features)

MRI Artifact Detection

CLAHE Enhancement

import cv2
import numpy as np

class MedicalCLAHE:
    def __init__(self, clip_limit=2.0, grid_size=8):
        self.clahe = cv2.createCLAHE(clipLimit=clip_limit,
                                     tileGridSize=(grid_size, grid_size))

    def enhance(self, image):
        if image.dtype == np.float32:
            image = (image * 255).astype(np.uint8)
        return self.clahe.apply(image)

Evaluation