Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SUPR-GAN: SUrgical PRediction GAN for Event Anticipation in Laparoscopic
  and Robotic Surgery   Comprehension of surgical workflow is the foundation upon which artificial
intelligence (AI) and machine learning (ML) holds the potential to assist
intraoperative decision-making and risk mitigation. In this work, we move
beyond mere identification of past surgical phases, into the prediction of
future surgical steps and specification of the transitions between them. We use
a novel Generative Adversarial Network (GAN) formulation to sample future
surgical phases trajectories conditioned on past video frames from laparoscopic
cholecystectomy (LC) videos and compare it to state-of-the-art approaches for
surgical video analysis and alternative prediction methods. We demonstrate the
GAN formulation's effectiveness through inferring and predicting the progress
of LC videos. We quantify the horizon-accuracy trade-off and explored average
performance, as well as the performance on the more challenging, and clinically
relevant transitions between phases. Furthermore, we conduct a survey, asking
16 surgeons of different specialties and educational levels to qualitatively
evaluate predicted surgery phases.

5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
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The categories of this paper are: hep-th
23
The categories of this paper are: solv-int