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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+ISIC_0000146.jpg filter=lfs diff=lfs merge=lfs -text

age_approx_scaler.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b3f5634fa2508e531c4196c06207c60c40de6f224e6ef73afc9b935242b18c2c
+size 623

app.py

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+import gradio as gr
+from PIL import Image
+from joblib import load
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision.models import efficientnet_b0
+import torchvision.transforms as transforms
+
+
+class MultiModalClassifier(nn.Module):
+    def __init__(self, num_classes, num_features):
+        super(MultiModalClassifier, self).__init__()
+        # Load pre-trained EfficientNet model
+        efficientnet = efficientnet_b0(pretrained=True)
+        
+        # Remove the last classifier layer
+        self.efficientnet_features = nn.Sequential(*list(efficientnet.children())[:-1])
+        
+        # Define additional feature dimensions
+        self.age_dim = 1  # assuming age is a single scalar value
+        self.anatom_site_dim = 1  # assuming anatomical site is a single scalar value
+        self.sex_dim = 1  # assuming sex is a single scalar value
+        
+        # Fully connected layers for classification
+        self.fc1 = nn.Linear(num_features + self.age_dim + self.anatom_site_dim + self.sex_dim, 256)
+        self.fc2 = nn.Linear(256, num_classes)
+        
+        # Dropout layer
+        self.dropout = nn.Dropout(p=0.5)
+        
+    def forward(self, image, age, anatom_site, sex):
+        # Forward pass through the pre-trained EfficientNet model
+        image_features = self.efficientnet_features(image)
+        image_features = F.avg_pool2d(image_features, image_features.size()[2:]).view(image.size(0), -1)  # Flatten
+        
+        # Reshape age, anatom_site, and sex tensors
+        age = age.view(-1, 1)  # Reshape to [batch_size, 1]
+        anatom_site = anatom_site.view(-1, 1)  # Reshape to [batch_size, 1]
+        sex = sex.view(-1, 1)  # Reshape to [batch_size, 1]
+        # Concatenate image features with additional features
+        additional_features = torch.cat((age, anatom_site, sex), dim=1)
+        combined_features = torch.cat((image_features, additional_features), dim=1)
+        
+        # Fully connected layers for classification
+        combined_features = F.relu(self.fc1(combined_features))
+        combined_features = self.dropout(combined_features)
+        output = self.fc2(combined_features) 
+        
+        return output
+
+# Initialize the model
+num_classes = 1  # Assuming binary classification
+num_features = 1280  # Number of features extracted by EfficientNet-B0
+model = MultiModalClassifier(num_classes, num_features)
+
+# Load the saved model state dictionary
+model.load_state_dict(torch.load(r'best_epoch_weights.pth',map_location=torch.device('cpu')))
+
+# Set the model to evaluation mode
+model.eval()
+
+# Load the age scaler
+age_scaler = load(r'age_approx_scaler.joblib')
+
+# Define transforms for the data (adjust as necessary)
+test_transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+])
+
+diagnosis_map = {0: 'benign', 1: 'malignant'} 
+
+# Define mapping dictionaries for sexes and anatom_sites
+sexes_mapping = {'male': 0, 'female': 1}
+
+# Define mapping dictionary for anatom_site_general
+anatom_site_mapping = {
+    'torso': 0,
+    'lower extremity': 1,
+    'head/neck': 2,
+    'upper extremity': 3,
+    'palms/soles': 4,
+    'oral/genital': 5,
+}
+
+def predict(image, age, gender, anatom_site):
+
+    image = Image.fromarray(image)
+    # Apply transformations to the image
+    image = test_transform(image)
+    image = image.float()
+    image = image.unsqueeze(0)  # Add batch dimension
+
+    sex = torch.tensor([[sexes_mapping[gender.lower()]]],  dtype=torch.float32)
+    anatom_site = torch.tensor([[anatom_site_mapping[anatom_site]]], dtype=torch.float32)
+
+    # Scale the age using the loaded scaler
+    scaled_age = age_scaler.transform([[age]])
+    # Convert scaled age to a tensor
+    age_tensor = torch.tensor(np.array(scaled_age), dtype=torch.float32)
+
+    # Forward pass
+    output = model(image, age_tensor, anatom_site, sex)
+
+    # Apply sigmoid to the output (since it's a binary classification)
+    output_sigmoid = torch.sigmoid(output)
+    # Get the predicted class (0 or 1)
+    predicted_class = (output_sigmoid > 0.5).float()
+
+    
+    return f"The predicted_class is a {diagnosis_map[int(predicted_class)]}."
+
+
+description_html = """
+Fill in the required parameters and click 'classify'.
+"""
+
+example_data = [
+    ["ISIC_0000060_downsampled.jpg", 35, "Female", "torso"],
+    ["ISIC_0068279.jpg", 45.0, "Female", "head/neck"]
+]
+
+inputs = [
+    "image",
+    gr.Number(label="Age", minimum=0, maximum=120),
+    gr.Dropdown(['Male', 'Female'], label="Gender"),
+    gr.Dropdown(['torso', 'lower extremity', 'head/neck', 'upper extremity', 'palms/soles', 'oral/genital'], label="Anatomical Site")
+]
+
+gr.Interface(
+    predict,
+    inputs,
+    outputs = gr.Textbox(label="Output", type="text"),
+    title="Skin Cancer Diagnosis",
+    description=description_html,
+    allow_flagging='never',
+    examples=example_data
+).launch()

best_epoch_weights.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4ab0618a9c77f2d85403b2c29988d3273ad991519678ceadc7ee16242bb5bb5f
+size 17659032

requirements.txt

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+gradio==3.39.0
+Pillow==9.5.0
+torch==2.2.1
+torchvision==0.17.1
+joblib==1.3.2
+numpy==1.26.2
+scikit-learn==1.2.2