Surgical Visual Understanding (SurgVU) Dataset
Aneeq Zia, Max Berniker, Rogerio Nespolo, Xiaorui Zhang, Conor Perreault, Ziheng Wang, Benjamin Mueller, Ryan Schmidt, Kiran Bhattacharyya, Xi Liu, and Anthony Jarc

TL;DR
This paper introduces the SurgVU dataset, a large collection of surgical videos and labels designed to advance machine learning research in surgical data science.
Contribution
It provides a comprehensive dataset with videos, labels, and auxiliary data to facilitate broad machine learning applications in surgical contexts.
Findings
Dataset includes diverse surgical videos and labels.
Multiple problem types can be addressed using this dataset.
Resources are publicly available for research use.
Abstract
Owing to recent advances in machine learning and the ability to harvest large amounts of data during robotic-assisted surgeries, surgical data science is ripe for foundational work. We present a large dataset of surgical videos and their accompanying labels for this purpose. We describe how the data was collected and some of its unique attributes. Multiple example problems are outlined. Although the dataset was curated for a particular set of scientific challenges (in an accompanying paper), it is general enough to be used for a broad range machine learning questions. Our hope is that this dataset exposes the larger machine learning community to the challenging problems within surgical data science, and becomes a touch-stone for future research. The videos are available at https://storage.googleapis.com/isi-surgvu/surgvu24_videos_only.zip, the labels at…
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