A framework for rigorous evaluation of human performance in human and machine learning comparison studies.

Rigorous comparisons of human and machine learning algorithm performance on the same task help to support accurate claims about algorithm success rates and advances understanding of their performance relative to that of human performers. In turn, these comparisons are critical for supporting advances in artificial intelligence. However, the machine learning community has lacked a standardized, consensus framework for performing the evaluations of human performance necessary for comparison.

Benchmarking Human Performance for Visual Search of Aerial Images.

Aerial images are frequently used in geospatial analysis to inform responses to crises and disasters but can pose unique challenges for visual search when they contain low resolution, degraded information about color, and small object sizes. Aerial image analysis is often performed by humans, but machine learning approaches are being developed to complement manual analysis. To date, however, relatively little work has explored how humans perform visual search on these tasks, and understanding this could ultimately help enable human-machine teaming.

Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets.

Background: Emerging neuroimaging datasets (collected with imaging techniques such as electron microscopy, optical microscopy, or X-ray microtomography) describe the location and properties of neurons and their connections at unprecedented scale, promising new ways of understanding the brain. These modern imaging techniques used to interrogate the brain can quickly accumulate gigabytes to petabytes of structural brain imaging data. Unfortunately, many neuroscience laboratories lack the computational resources to work with datasets of this size: computer vision tools are often not portable or scalable, and there is considerable difficulty in reproducing results or extending methods.

Bioimage Informatics for Big Data.

Bioimage informatics is a field wherein high-throughput image informatics methods are used to solve challenging scientific problems related to biology and medicine. When the image datasets become larger and more complicated, many conventional image analysis approaches are no longer applicable. Here, we discuss two critical challenges of large-scale bioimage informatics applications, namely, data accessibility and adaptive data analysis.