Topic recognition

.gitignore

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+HipMRI_study_keras_slices_data
+_pycache_

LICENSE

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+=======
                                  Apache License
                            Version 2.0, January 2004
                         http://www.apache.org/licenses/
@@ -199,3 +402,4 @@
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.
+>>>>>>> f0b762688b9a777aad0204f369487ca412f1fc5f

README.md

@@ -1,15 +1,71 @@
-# Pattern Analysis
-Pattern Analysis of various datasets by COMP3710 students at the University of Queensland.
-
-We create pattern recognition and image processing library for Tensorflow (TF), PyTorch or JAX.
-
-This library is created and maintained by The University of Queensland [COMP3710](https://my.uq.edu.au/programs-courses/course.html?course_code=comp3710) students.
-
-The library includes the following implemented in Tensorflow:
-* fractals 
-* recognition problems
-
-In the recognition folder, you will find many recognition problems solved including:
-* OASIS brain segmentation
-* Classification
-etc.
+# Pattern Analysis
+Pattern Analysis of various datasets by COMP3710 students in 2024 at the University of Queensland.
+
+We create pattern recognition and image processing library for Tensorflow (TF), PyTorch or JAX.
+
+This library is created and maintained by The University of Queensland [COMP3710](https://my.uq.edu.au/programs-courses/course.html?course_code=comp3710) students.
+
+The library includes the following implemented in Tensorflow:
+* fractals 
+* recognition problems
+
+In the recognition folder, you will find many recognition problems solved including:
+* segmentation
+* classification
+* graph neural networks
+* StyleGAN
+* Stable diffusion
+* transformers
+etc.
+
+# VQ-VAE for Medical Image Generation
+## Description
+This repository implements a Vector Quantized Variational Autoencoder (VQ-VAE) model to generate medical images from 2D slices of prostate MRI scans. The VQ-VAE is a generative model that combines the principles of variational inference with vector quantization, allowing for a discrete representation that improves the quality of image reconstructions. The goal of this model is to map MRI scans of Prostate Cancer into a lower-dimensional latent space and decode the quantized representation back to the original image, providing a method for generating clear MRI images with improved similarity to real medical data.
+
+## Problem
+The challenge this VQ-VAE addresses is generating clear medical images from noisy data while maintaining high similarity to the original MRI scans. Specifically, the model is trained on the HipMRI Study dataset containing 2D prostate MRI slices and attempts to reconstruct these images with a focus on structural similarity.
+
+## How It Works
+The VQ-VAE model works by encoding input images, which is then quantized to discrete vectors. 
+This quantized space is then decoded to reconstruct the original image. The key components include:
+
+1. Encoder: Converts input images into a lower-dimensional latent representation using convolutional layers.
+2. Vector Quantizer: Discretizes the latent space by mapping the continuous latent vectors to the nearest embedding in the codebook.
+3. Decoder: Reconstructs the input image from the quantized latent vectors using transposed convolution layers.
+4. Loss Function: The model optimizes a combination of reconstruction loss (MSE) and a commitment loss to ensure the latent vectors stay close to the embeddings.
+
+## A high-level flow:
+* Input MRI image -> Encoder -> Latent Space
+* Latent Space -> Vector Quantizer -> Quantized Latent Space
+* Quantized Latent Space -> Decoder -> Reconstructed MRI image
+
+## Dependencies
+To install all dependecies, run:
+pip install torch torchvision nibabel matplotlib tqdm pathlib 
+
+## Usage
+1. Preprocessing: The .nii MRI image files are sliced into 2D images and resized. Normalization to the range [0, 1] is applied
+2. Training: The dataset is split into 70% for training, 15% for validation, and 15% for testing. This split is chosen to ensure a sufficiently large training set while keeping a balanced validation and test set for hyperparameter tuning and performance evaluation.
+3. To Run the training on current variables and epochs, while saving the model to reconstruct the images later
+* > python train.py -save
+4. To reconstruct the images after training and save the reconstructed images (top 5)
+* > python predict.py -save
+
+## Results
+### Validation SSIM
+The model achieves a structural similarity index (SSIM) of 0.75 averaged over 5 images, indicating good image quality and resemblence of the input MRI Scans
+
+## Working Principles
+### Algorithm:
+* The VQ-VAE model solves the problem of medical image generation by learning discrete latent representations and creates high-quality MRI reconstructions
+### Comments:
+* All key components and methods in the modules.py file and all other files are well-commented and structures to explain the core logic behind the model
+### Formatting:
+* The README is properly formatted using GitHub markdown
+
+## Examples:
+* Examples of the model's reconstructed images can be found at ./results
+
+# Author
+Harrison Cleland, 2024
+47433386

dataset.py

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+import torch
+import numpy as np
+import nibabel as nib
+from tqdm import tqdm
+
+# Set device for PyTorch
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Convert array to one-hot encoding
+def to_channels(arr: np.ndarray, dtype=np.uint8) -> np.ndarray:
+    unique_classes = np.unique(arr)  # Find unique classes
+    one_hot = np.zeros(arr.shape + (len(unique_classes),), dtype=dtype)  # Initialize one-hot array
+    for c in unique_classes:
+        c = int(c)
+        one_hot[..., c:c + 1][arr == c] = 1  # Set one-hot encoding
+
+    return one_hot
+
+# Load 2D medical image data
+def load_data_2D(imageNames, normImage=False, categorical=False, dtype=np.float32,
+                 getAffines=False, early_stop=False):
+    '''
+    Load medical image data from provided filenames.
+
+    normImage: bool (normalize image to 0.0 - 1.0)
+    early_stop: stop loading prematurely for quick tests
+    '''
+
+    affines = []  # Store affine transformations
+
+    num = len(imageNames)  # Number of images
+    print("Length of Images: ", num)
+    first_case = nib.load(imageNames[0]).get_fdata(caching='unchanged')  # Load first image
+    if len(first_case.shape) == 3:
+        first_case = first_case[:, :, 0]  # Take first slice if 3D
+    if categorical:
+        first_case = to_channels(first_case, dtype=dtype)  # Convert to one-hot if categorical
+        rows, cols, channels = first_case.shape
+        images = np.zeros((num, rows, cols, channels), dtype=dtype)  # Pre-allocate images
+    else:
+        rows, cols = first_case.shape
+        images = np.zeros((num, rows, cols), dtype=dtype)  # Pre-allocate images
+
+    # Load each image
+    for i, inName in enumerate(tqdm(imageNames)):
+        try:
+            niftiImage = nib.load(inName)  # Load NIfTI image
+            inImage = niftiImage.get_fdata(caching='unchanged')  # Get image data
+            affine = niftiImage.affine  # Get affine transformation
+            if len(inImage.shape) == 3:
+                inImage = inImage[:, :, 0]  # Take first slice if 3D
+            inImage = inImage.astype(dtype)  # Convert to specified dtype
+            if normImage:
+                inImage = (inImage - inImage.mean()) / inImage.std()  # Normalize image
+            if categorical:
+                inImage = to_channels(inImage, dtype=dtype)  # Convert to one-hot if categorical
+                images[i, :, :, :] = inImage  # Store in images array
+            else:
+                images[i, :, :] = inImage  # Store in images array
+
+            affines.append(affine)  # Store affine
+            if i > 20 and early_stop:
+                break  # Early stop if set
+        except:
+            print("Error occurred on image: ", i, inName)  # Error handling
+
+    if getAffines:
+        return images, affines  # Return images and affines if requested
+    else:
+        return images  # Return only images