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An artificial intelligence (AI) using a deep-learning approach can classify retinal images from optical coherence tomography for early diagnosis of retinal diseases and has the potential to be used in other image-based medical diagnoses.   ...more

To properly evaluate the ability of robots to operate autonomously in the real world, it is necessary to develop methods for quantifying their self-righting capabilities. Here, we improve upon a sampling-based framework for evaluating self-righting capabilities that was previously validated in two dimensions. To apply this framework to realistic r   ...more

Background: Surgical management of colorectal cancer relies on accurate identification of tumor and possible metastatic disease. Hyperspectral (HS) sensing is a passive, non‐ionizing diagnostic method that has been considered for multiple tumor types. The ability to use HS for identification of tumor specimens during surgical resection of colorect   ...more

Purpose: Cone-beam computed tomography (CBCT) is one of the primary imaging modalities in radiation therapy, dentistry, and orthopedic interventions. While CBCT provides crucial intraoperative information, it is bounded by a limited imaging volume, resulting in reduced effectiveness. This paper introduces an approach allowing real-time intraoperat   ...more

Optical neuroimaging technologies aim to observe neural tissue structure and function by detecting changes in optical signals (scatter, absorption, etc…) that accompany a range of anatomical and functional properties of brain tissue. At present, there is a tradeoff between spatial and temporal resolution that is not currently optimized in a single   ...more

We describe NDStore, a scalable multi-hierarchical data storage deployment for spatial analysis of neuroscience data on the AWS cloud. The system design is inspired by the requirement to maintain high I/O throughput for workloads that build neural connectivity maps of the brain from peta-scale imaging data using comput   ...more

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