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Surgical data science for next-generation interventions.

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TLDR
Interventional healthcare will evolve from an artisanal craft based on the individual experiences, preferences and traditions of physicians into a discipline that relies on objective decision-making on the basis of large-scale data from heterogeneous sources.
Abstract
Interventional healthcare will evolve from an artisanal craft based on the individual experiences, preferences and traditions of physicians into a discipline that relies on objective decision-making on the basis of large-scale data from heterogeneous sources.

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Gesture Recognition in Robotic Surgery: A Review

TL;DR: In this article, the state-of-the-art in methods for automatic recognition of fine-grained gestures in robotic surgery focusing on recent data-driven approaches and outlines the open questions and future research directions.
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Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy

TL;DR: This paper significantly enhances the state of art in automatic labeling of endoscopic videos by introducing the use of the confidence metric, and by being the first study to use MI data for in vivo laparoscopic tissue classification.
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CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions

TL;DR: In this paper, the authors highlight the crucial need to take context and human factors into account in order to address these challenges, and propose Contextual Artificial Intelligence for Computer-Aided Interventions (CAI4CAI).
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Surgical motion analysis using discriminative interpretable patterns.

TL;DR: The proposed approach for the discovery and ranking of discriminative and interpretable patterns of surgical practice from recordings of surgical motions is an interesting addition to existing learning tools for surgery as it provides a feedback on which parts of an exercise have been used to classify the attempt as correct or incorrect.
References
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Journal ArticleDOI

Gene Ontology: tool for the unification of biology

TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
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ImageNet Large Scale Visual Recognition Challenge

TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
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Machine learning: Trends, perspectives, and prospects

TL;DR: The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
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Deep Learning in Medical Image Analysis

TL;DR: This review covers computer-assisted analysis of images in the field of medical imaging and introduces the fundamentals of deep learning methods and their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on.
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An estimation of the global volume of surgery: a modelling strategy based on available data

TL;DR: In view of the high death and complication rates of major surgical procedures, surgical safety should now be a substantial global public-health concern.
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