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9916246 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import torch
import torch.nn as nn
from torchvision.models import resnet50, ResNet50_Weights
class CliniScanClassifier(nn.Module):
def __init__(self, num_classes=15, freeze_features=True):
"""
Original ResNet50 implementation for AI-CliniScan abnormality classification.
"""
super(CliniScanClassifier, self).__init__()
# Load pretrained ResNet50
self.backbone = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)
if freeze_features:
for param in self.backbone.parameters():
param.requires_grad = False
# Unfreeze layer4 for fine-tuning
for param in self.backbone.layer4.parameters():
param.requires_grad = True
in_features = self.backbone.fc.in_features
# Replace the fully connected layer for multi-label classification
self.backbone.fc = nn.Sequential(
nn.Dropout(p=0.3),
nn.Linear(in_features, 512),
nn.ReLU(),
nn.Dropout(p=0.3),
nn.Linear(512, num_classes)
)
def forward(self, x):
return self.backbone(x)
def extract_features(self, x):
"""Used for Grad-CAM Visualization"""
x = self.backbone.conv1(x)
x = self.backbone.bn1(x)
x = self.backbone.relu(x)
x = self.backbone.maxpool(x)
x = self.backbone.layer1(x)
x = self.backbone.layer2(x)
x = self.backbone.layer3(x)
features = self.backbone.layer4(x)
return features
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