Thrombosis

Libraries

Tensorflow
Keras
matplotlib
sklearn
numpy
scipy

Processing Pipeline

1. Data ID CSV & File restructuring

File: restructure v2

Each from the raw data source is renamed to a unique id, and stored in the root folder as a numpy array. The ids are stored in a csv called ids.csv

2. Augmentation

3. Feature Extraction

File: Feature Extraction

We extract features from the raw numpy arrays, read as images, and stored the metrics in the ids.csv file in line with the id of the numpy array.

Linear Regression

Image -> Flow Rate

File: Image Logistic Regression Keras

Simple Logistic Regression on the image to predict flow rate.

Features -> Flow rate

File: Logistic Regression Keras

Logistic Regression applied to the extracted features to predict flow rate. Also completed using cross-validation and the result is visualised through a confusion matrix.

CNN

Currently not working since it's on the

K-Means

File: K-Means

Visualisations

File: K-Means

Visualisation of the extracted features. Visualisations include: Pearsons Corrolation Coefficient. Scatter/Histograms of individual features. Graphs of t-SNE and other dimensionality reduction methods.

Name		Name	Last commit message	Last commit date
Latest commit History 21 Commits
images		images
sherrys code		sherrys code
smoothing_app		smoothing_app
.gitattributes		.gitattributes
.gitignore		.gitignore
AutoML Extracted Donor.ipynb		AutoML Extracted Donor.ipynb
AutoML Extracted.ipynb		AutoML Extracted.ipynb
AutoML Image.ipynb		AutoML Image.ipynb
Biomarker Analysis Heparin.ipynb		Biomarker Analysis Heparin.ipynb
CNN-Test-Iris.ipynb		CNN-Test-Iris.ipynb
CNN.ipynb		CNN.ipynb
Cluster Logistic Regression Keras.ipynb		Cluster Logistic Regression Keras.ipynb
Contrast Measure - Sine.ipynb		Contrast Measure - Sine.ipynb
Contrast Measurements.ipynb		Contrast Measurements.ipynb
Convolutional_Autoencoder.ipynb		Convolutional_Autoencoder.ipynb
DNN Keras.ipynb		DNN Keras.ipynb
FFT Image Saver.ipynb		FFT Image Saver.ipynb
Feature Analysis by Donor Citrate PRP.ipynb		Feature Analysis by Donor Citrate PRP.ipynb
Feature Analysis by Donor Heparin.ipynb		Feature Analysis by Donor Heparin.ipynb
Feature Analysis.ipynb		Feature Analysis.ipynb
Feature Extraction Heparin.ipynb		Feature Extraction Heparin.ipynb
Feature Extraction.ipynb		Feature Extraction.ipynb
First.ipynb		First.ipynb
Fourier Simulation.ipynb		Fourier Simulation.ipynb
Gradiant Boosted Forest Extracted-Copy1.ipynb		Gradiant Boosted Forest Extracted-Copy1.ipynb
Gradiant Boosted Forest Extracted.ipynb		Gradiant Boosted Forest Extracted.ipynb
HoloUNet.ipynb		HoloUNet.ipynb
Hologram Propogation.ipynb		Hologram Propogation.ipynb
Hologram Simulation.ipynb		Hologram Simulation.ipynb
Image Logistic Regression Keras.ipynb		Image Logistic Regression Keras.ipynb
Keras t-SNE.ipynb		Keras t-SNE.ipynb
Kolmogorov–Smirnov test.ipynb		Kolmogorov–Smirnov test.ipynb
Logistic Regression Keras Test Iris.ipynb		Logistic Regression Keras Test Iris.ipynb
Logistic Regression Keras Two Features.ipynb		Logistic Regression Keras Two Features.ipynb
Logistic Regression Keras by Donor Heparin.ipynb		Logistic Regression Keras by Donor Heparin.ipynb
Logistic Regression Keras by Donor Single Feature.ipynb		Logistic Regression Keras by Donor Single Feature.ipynb
Logistic Regression Keras by Donor.ipynb		Logistic Regression Keras by Donor.ipynb
Logistic Regression Keras.ipynb		Logistic Regression Keras.ipynb
Logistic Regression sci-kit learn.ipynb		Logistic Regression sci-kit learn.ipynb
Logistic Regression.ipynb		Logistic Regression.ipynb
Noise Adding.ipynb		Noise Adding.ipynb
Noise Generation v2.ipynb		Noise Generation v2.ipynb
Noise removal - multiple frames.ipynb		Noise removal - multiple frames.ipynb
PCA - Feature.ipynb		PCA - Feature.ipynb
Phase Recovery.ipynb		Phase Recovery.ipynb
Phase changing.ipynb		Phase changing.ipynb
Propagation.ipynb		Propagation.ipynb
README.md		README.md
Random Forest Extracted with LIME.ipynb		Random Forest Extracted with LIME.ipynb
Random Forest Extracted.ipynb		Random Forest Extracted.ipynb
Random Forest Image.ipynb		Random Forest Image.ipynb
Soma.ipynb		Soma.ipynb
Spherical Diffraction.ipynb		Spherical Diffraction.ipynb
Untitled.ipynb		Untitled.ipynb
Untitled1.ipynb		Untitled1.ipynb
Untitled2.ipynb		Untitled2.ipynb
opticspy.ipynb		opticspy.ipynb
out.bmp		out.bmp
restructure tif.ipynb		restructure tif.ipynb
restructure v2 Heparin.ipynb		restructure v2 Heparin.ipynb
restructure v2.ipynb		restructure v2.ipynb
restructure.ipynb		restructure.ipynb
tpot_mnist_pipeline.py		tpot_mnist_pipeline.py
trace.avi		trace.avi

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Thrombosis

Libraries

Processing Pipeline

1. Data ID CSV & File restructuring

2. Augmentation

3. Feature Extraction

Linear Regression

Image -> Flow Rate

Features -> Flow rate

CNN

K-Means

Visualisations

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zhiduozhang/holo-unet

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Thrombosis

Libraries

Processing Pipeline

1. Data ID CSV & File restructuring

2. Augmentation

3. Feature Extraction

Linear Regression

Image -> Flow Rate

Features -> Flow rate

CNN

K-Means

Visualisations

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