Inspiration
Neuroscience is a field that is being augmented by recent work in deep learning. This project presents one such way in which traditional neuroscience research can be assisted by deep learning technologies.
What it does
We built a model that augments microscopic images of neuron clusters to improve downstream accuracy of an image segmentation model which identifies neurons from their surroundings.
How we built it
We used Python and Keras in addition to outside data science and computer vision libraries like Numpy and PIL to explore, transform, and augment our data
Challenges we ran into
There were lots of issues with data cleanliness that we had to resolve. An interesting challenge related to visualizing the data involved processing it such that the inputs and outputs of the model could actually be interpreted visually. Understanding these differences was insightful in data augmentation and modifying inputs.
Accomplishments that we're proud of
We managed to marginally improve accuracy.
What we learned
We learned how to use computer vision in combination with deep learning.
What's next for SnekNet Brain Segmentation
Flood-filling or other pre-processing
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