Project 5: Multimodal Medical Image and Report Analysis with Vision-Language Models
Step 2: Load and Preprocess the Data
Load and preprocess the medical images and their associated text reports. This crucial step involves reading the image files, converting them to the appropriate format (RGB), and organizing the corresponding text reports.
During preprocessing, we ensure that images are properly scaled and normalized, while text reports are cleaned and structured consistently. For simplicity in this demonstration, we will preprocess a sample dataset, though in a production environment you would typically work with much larger datasets requiring more sophisticated preprocessing pipelines and data validation steps.
import os
from PIL import Image
from transformers import CLIPProcessor
# Directory paths
image_dir = "path_to_medical_images"
text_reports = {
"image_1.jpg": "This X-ray shows signs of pneumonia.",
"image_2.jpg": "Normal chest radiograph with no abnormalities.",
}
# Load and preprocess images
images = []
captions = []
for image_name, caption in text_reports.items():
image_path = os.path.join(image_dir, image_name)
image = Image.open(image_path).convert("RGB")
images.append(image)
captions.append(caption)
# Initialize CLIP processor
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
inputs = processor(text=captions, images=images, return_tensors="pt", padding=True)Let's break down this code that handles medical image and text preprocessing:
1. Imports and Setup
- The code imports necessary libraries:
- os: for file path operations
- PIL (Python Imaging Library): for image processing
- CLIPProcessor: for preparing data for the CLIP model
2. Data Structure
- Creates a dictionary 'text_reports' that maps image filenames to their corresponding medical descriptions (e.g., X-ray findings)
3. Data Processing Loop
- Iterates through the image-text pairs to:
- Load each image and convert it to RGB format
- Store images and their captions in separate lists
- Create proper file paths for each image
4. CLIP Processing
- Initializes the CLIP processor and prepares the data by:
- Converting both images and text into a format suitable for the CLIP model
- Creating tensor representations with proper padding
- Returning the processed inputs ready for model usage
This preprocessing step is crucial for the system's ability to match medical images with their corresponding reports and generate accurate descriptions.
Step 2: Load and Preprocess the Data
Load and preprocess the medical images and their associated text reports. This crucial step involves reading the image files, converting them to the appropriate format (RGB), and organizing the corresponding text reports.
During preprocessing, we ensure that images are properly scaled and normalized, while text reports are cleaned and structured consistently. For simplicity in this demonstration, we will preprocess a sample dataset, though in a production environment you would typically work with much larger datasets requiring more sophisticated preprocessing pipelines and data validation steps.
import os
from PIL import Image
from transformers import CLIPProcessor
# Directory paths
image_dir = "path_to_medical_images"
text_reports = {
"image_1.jpg": "This X-ray shows signs of pneumonia.",
"image_2.jpg": "Normal chest radiograph with no abnormalities.",
}
# Load and preprocess images
images = []
captions = []
for image_name, caption in text_reports.items():
image_path = os.path.join(image_dir, image_name)
image = Image.open(image_path).convert("RGB")
images.append(image)
captions.append(caption)
# Initialize CLIP processor
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
inputs = processor(text=captions, images=images, return_tensors="pt", padding=True)Let's break down this code that handles medical image and text preprocessing:
1. Imports and Setup
- The code imports necessary libraries:
- os: for file path operations
- PIL (Python Imaging Library): for image processing
- CLIPProcessor: for preparing data for the CLIP model
2. Data Structure
- Creates a dictionary 'text_reports' that maps image filenames to their corresponding medical descriptions (e.g., X-ray findings)
3. Data Processing Loop
- Iterates through the image-text pairs to:
- Load each image and convert it to RGB format
- Store images and their captions in separate lists
- Create proper file paths for each image
4. CLIP Processing
- Initializes the CLIP processor and prepares the data by:
- Converting both images and text into a format suitable for the CLIP model
- Creating tensor representations with proper padding
- Returning the processed inputs ready for model usage
This preprocessing step is crucial for the system's ability to match medical images with their corresponding reports and generate accurate descriptions.
Step 2: Load and Preprocess the Data
Load and preprocess the medical images and their associated text reports. This crucial step involves reading the image files, converting them to the appropriate format (RGB), and organizing the corresponding text reports.
During preprocessing, we ensure that images are properly scaled and normalized, while text reports are cleaned and structured consistently. For simplicity in this demonstration, we will preprocess a sample dataset, though in a production environment you would typically work with much larger datasets requiring more sophisticated preprocessing pipelines and data validation steps.
import os
from PIL import Image
from transformers import CLIPProcessor
# Directory paths
image_dir = "path_to_medical_images"
text_reports = {
"image_1.jpg": "This X-ray shows signs of pneumonia.",
"image_2.jpg": "Normal chest radiograph with no abnormalities.",
}
# Load and preprocess images
images = []
captions = []
for image_name, caption in text_reports.items():
image_path = os.path.join(image_dir, image_name)
image = Image.open(image_path).convert("RGB")
images.append(image)
captions.append(caption)
# Initialize CLIP processor
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
inputs = processor(text=captions, images=images, return_tensors="pt", padding=True)Let's break down this code that handles medical image and text preprocessing:
1. Imports and Setup
- The code imports necessary libraries:
- os: for file path operations
- PIL (Python Imaging Library): for image processing
- CLIPProcessor: for preparing data for the CLIP model
2. Data Structure
- Creates a dictionary 'text_reports' that maps image filenames to their corresponding medical descriptions (e.g., X-ray findings)
3. Data Processing Loop
- Iterates through the image-text pairs to:
- Load each image and convert it to RGB format
- Store images and their captions in separate lists
- Create proper file paths for each image
4. CLIP Processing
- Initializes the CLIP processor and prepares the data by:
- Converting both images and text into a format suitable for the CLIP model
- Creating tensor representations with proper padding
- Returning the processed inputs ready for model usage
This preprocessing step is crucial for the system's ability to match medical images with their corresponding reports and generate accurate descriptions.
Step 2: Load and Preprocess the Data
Load and preprocess the medical images and their associated text reports. This crucial step involves reading the image files, converting them to the appropriate format (RGB), and organizing the corresponding text reports.
During preprocessing, we ensure that images are properly scaled and normalized, while text reports are cleaned and structured consistently. For simplicity in this demonstration, we will preprocess a sample dataset, though in a production environment you would typically work with much larger datasets requiring more sophisticated preprocessing pipelines and data validation steps.
import os
from PIL import Image
from transformers import CLIPProcessor
# Directory paths
image_dir = "path_to_medical_images"
text_reports = {
"image_1.jpg": "This X-ray shows signs of pneumonia.",
"image_2.jpg": "Normal chest radiograph with no abnormalities.",
}
# Load and preprocess images
images = []
captions = []
for image_name, caption in text_reports.items():
image_path = os.path.join(image_dir, image_name)
image = Image.open(image_path).convert("RGB")
images.append(image)
captions.append(caption)
# Initialize CLIP processor
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
inputs = processor(text=captions, images=images, return_tensors="pt", padding=True)Let's break down this code that handles medical image and text preprocessing:
1. Imports and Setup
- The code imports necessary libraries:
- os: for file path operations
- PIL (Python Imaging Library): for image processing
- CLIPProcessor: for preparing data for the CLIP model
2. Data Structure
- Creates a dictionary 'text_reports' that maps image filenames to their corresponding medical descriptions (e.g., X-ray findings)
3. Data Processing Loop
- Iterates through the image-text pairs to:
- Load each image and convert it to RGB format
- Store images and their captions in separate lists
- Create proper file paths for each image
4. CLIP Processing
- Initializes the CLIP processor and prepares the data by:
- Converting both images and text into a format suitable for the CLIP model
- Creating tensor representations with proper padding
- Returning the processed inputs ready for model usage
This preprocessing step is crucial for the system's ability to match medical images with their corresponding reports and generate accurate descriptions.