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MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
Jorge Quesada · Lakshmi Sathidevi · Ran Liu · Nauman Ahad · Joy Jackson · Mehdi Azabou · Jingyun Xiao · Christopher Liding · Matthew Jin · Carolina Urzay · William Gray-Roncal · Erik Johnson · Eva Dyer

Tue Nov 29 09:00 AM -- 11:00 AM (PST) @ Hall J #1019

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/.

Author Information

Jorge Quesada (Georgia Institute of Technology)
Lakshmi Sathidevi (Georgia Institute of Technology)
Ran Liu (Georgia Institute of Technology)

I am a 4th year Ph.D. student in the Machine Learning Program at Georgia Tech. I conduct my research in the Neural Data Science Lab advised by Prof. Eva Dyer. My research interests lie at the intersection of Machine (Deep) Learning, Computational Neuroscience, and Computer Vision.

Nauman Ahad (Georgia Institute of Technology)
Joy Jackson (University of Miami)
Mehdi Azabou (Georgia Institute of Technology)
Jingyun Xiao (Georgia Institute of Technology)
Christopher Liding (Georgia Institute of Technology)
Matthew Jin
Carolina Urzay (Georgia Institute of Technology)
William Gray-Roncal (Johns Hopkins University)
Erik Johnson (Johns Hopkins University Applied Physics Laboratory)
Eva Dyer (Georgia Institute of Technology)

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