output
20192026
most citedWeakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images

185 citations

6 papers

cs.HC2026

Integrating Virtual Reality and Large Language Models for Team-Based Non-Technical Skills Training and Evaluation in the Operating Room

Jacob Barker, Doga Demirel, Cullen Jackson +7

Although effective teamwork and communication are critical to surgical safety, structured training for non-technical skills (NTS) remains limited compared with technical simulation…

cs.CV20235 cited

Deep learning-based estimation of whole-body kinematics from multi-view images

Kien X. Nguyen, Liying Zheng, Ashley L. Hawke +4

It is necessary to analyze the whole-body kinematics (including joint locations and joint angles) to assess risks of fatal and musculoskeletal injuries in occupational tasks. Human…

q-bio.OT20214 cited

Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

Lauren M. Sanders, Jason H. Yang, Ryan T. Scott +53

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengin…

q-bio.OT20218 cited

Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

Ryan T. Scott, Erik L. Antonsen, Lauren M. Sanders +53

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in d…

cs.CV2020185 cited

Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images

Hui Qu, Pengxiang Wu, Qiaoying Huang +7

Nuclei segmentation is a fundamental task in histopathology image analysis. Typically, such segmentation tasks require significant effort to manually generate accurate pixel-wise a…

cs.CV20194 cited

A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images

Yunzhe Xue, Fadi G. Farhat, Olga Boukrina +4

Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis and treatment planning. We prop…