From an exchange with Dr. Seung, an expert on connectomics, I learned that one of his former PhD students Viren Jain is leading the Connectomic effort at Google using supervised learning methods. Now, supervised deep learning methods usually require large amounts of data to succeed. (I know this based on experience in industry as well as the deep learning literature.) However, due to the time and effort of human annotation the datasets for circuit reconstruction are really small:
In fact, Dr. Seung informed me that the following dataset from a circuit reconstruction competition in 2016 which is less than five gigabytes in total is basically state of the art.
Given that augmenting training data with synthetic data is frequently used for computer vision problems, I am led to the following questions:
- Might it theoretically be possible to augment human-annotated training data with morphologically similar synthetic data?
- What methods in the near future might allow us to significantly increase the size of human-annotated training data?
- Michał Januszewski, Jörgen Kornfeld, Peter H. Li, Art Pope, Tim Blakely, Larry Lindsey, Jeremy Maitin-Shepard, Mike Tyka, Winfried Denk & Viren Jain. High-precision automated reconstruction of neurons with flood-filling networks. Nature. 2018.
- Luis Perez and Jason Wang. The Effectiveness of Data Augmentation in Image Classification using Deep Learning. Arxiv. 2017.
- Alberto Bailoni, Constantin Pape, Steffen Wolf, Thorsten Beier, Anna Kreshuk, Fred A. Hamprecht. A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation. Arxiv. 2019.
- EyeWire: mapping neurons. https://science.eyewire.org/science-mapping-neurons Accessed 21 August 2019.