Dataset: "Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis"

Published: 6 September 2021| Version 1 | DOI: 10.17632/sv5m953vjj.1
Joost Verduijn


In this repository you will find all data, models and MATLAB code used with this publication; DOI: (pending). This is used for cell death detection on DHM (Digital holographic microscopy) data using deep learning in MATLAB. This processing pipeline consists of a few different parts. 1) SAD.m Supervised Anomaly Detection in order to remove all Alive cells from cell death experiments and vice versa. This since cell death induction (like any other biological process) is happening not at the same time; the cell population is heterogeneous. SAD filters and homogenizes the dataset, the SAD filter is stored in Models/SAD_filter.mat 2) CropFromCaptureAndFilter.m This script takes the captures (whole field of view, from folder Captures) of the DHM camera snaps and crops out the cells from each capture. The single cells are stored in CroppedImages 3) These CroppedImages are split in HoldoutSet and TrainingSet 4) MakeModelViaTransferLearning.m Uses Transfer learning to reuse the VGG-19 network and repurpose this to predict cell death on these data. This script relearns the model on the trainingset from the folder TrainingSet and saves the model under Models/convnet.mat 5) UseModelOnHoldoutSet.m Opens ConvNet model (created by MakeModelViaTransferLearning.m) and the HoldoutSet (independent experiments) and runs the model on these images. Later on the overall accuracy is measured (total correct predictions/total samples *100%) and a confusion matrix is made. Finally an ROC plot is made.



Universiteit Gent


Cell Biology, Cell Death, Deep Learning