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David Feaster
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hemorrhage-detection
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hemorrhage-detection

This repository contains our implementation and training of a combined recurrent-convolutional DNN for intracranial hemorrhage (bleeding inside the brain) detection on CT scans.

The architecture that we have developed vastly outperforms the standard convolution-only approach: Our model achieves a recall (that is, it correctly detects bleeding) of 94% compared to a recall of 73% when only using a CNN.

The recurrent neural network enables our model to incorporate information from CT slices of the brain above and below the current slice when predicting whether the current slice contains a hemorrhage or not. Of course, surrounding slices containing hemorrhages make it more likely for the current slice to also contain one (especially when the slices right above and right below show bleeding).

This work is a collaboration of Richard Qiu, Philippe Noël, Jonathan Berman, and Jorma Görns.

Contents

  • "EDA and Data Cleaning.ipynb": Contains our EDA, data cleaning, and shows how we reconstructed complete CT scans of all patients from disjoint DICOM files

  • "Windowing PNGs.ipynb": When examining brain CT scans, radiologists rarely look at the raw images (they appear mostly gray to the human eye). Instead, they use so-called "windows"---simple transformations of the raw data that serve to highlight structures of different density in the human brain. The three most common windows for hemorrhage detection are the bone, brain, and subdural window. These are also the three windows that we apply to help our model detect hemorrhages. Specifically, we read in black-and-white, one-channel PNGs and turn them into RGB, three-channel PNGs where each channel contains one specific window.

  • "Training the CNN.ipynb": Details the training of our feature extractor (specifically, a VGG19 CNN that we train to detect hemorrhages using transfer learning)

  • "Feature Extraction.ipynb": We extract the features for the training of our recurrent neural network by feeding all training PNGs to our previously-trained CNN, letting it run its inference, and---for every PNG---grabbing 8192 features from the CNN's last convolutional block

  • "Training the R-CNN.ipynb": Details the training of our final model, a bidirectional LSTM using features extracted by the CNN

  • Finally, the docs folder also contains a verbal report of this project.