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<!DOCTYPE html>
<html>
<head lang="en">
<meta charset="UTF-8">
<meta http-equiv="x-ua-compatible" content="ie=edge">
<title>NETS</title>
<meta name="description" content="">
<meta name="viewport" content="width=device-width, initial-scale=1">
<!-- <base href="/"> -->
<link rel="apple-touch-icon" href="apple-touch-icon.png">
<!-- Place favicon.ico in the root directory -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap.min.css">
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css">
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.8.0/codemirror.min.css">
<link rel="stylesheet" href="css/app.css">
<link rel="stylesheet" type="text/css" href="//fonts.googleapis.com/css?family=Didact+Gothic" />
<link rel="stylesheet" href="css/bootstrap.min.css">
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.3/jquery.min.js"></script>
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<script src="js/app.js"></script>
</head>
<body font-family: 'Didact Gothic'>
<div class="container">
<div class="row">
<h2 class="col-md-12 text-center" font-family: 'Didact Gothic'>
Representation Learning Using Rank Loss for Robust Neurosurgical Skills Evaluation
</br>
<small>
ICIP 2022
</small>
</h2>
</div>
<div class="row">
<div class="col-md-12 text-center">
<ul class="list-inline">
<li>
<a href="https://www.cse.iitd.ac.in/~britty">
Britty Baby
</a>
</br>IIT Delhi
</li>
<li>
<a href="mailto:[email protected]">
Mustafa Chasmai
</a>
</br>IIT Delhi
<li>
<a href="mailto:[email protected]">
Tamajit Banerjee
</a>
</br>IIT Delhi
</li>
<li>
<a href="mailto:[email protected]">
Ashish Suri
</a>
</br>AIIMS New Delhi
</li>
<li>
<a href="https://www.cse.iitd.ac.in/~suban">
Subhashis Banerjee
</a>
</br>IIT Delhi
</li>
<li>
<a href="https://www.cse.iitd.ac.in/~chetan">
Chetan Arora
</a>
</br>IIT Delhi
</li>
</ul>
</div>
</div>
<div class="row" align="middle">
<div class="btn-group" role="group" aria-label="Top menu" align="middle">
<a class="btn btn-primary" href="https://arxiv.org/">Paper</a>
<a class="btn btn-primary" href="https://github.com/myselfbritty/NeuroEval">Code</a>
<a class="btn btn-primary" href="https://nets-iitd.github.io/NETS_V1/nets_v1_data">Dataset</a>
</div>
</div>
<br><br>
<div class="row" id="header_img" align="middle">
<image src="nets-data/teaser.jpg" class="img-responsive" alt="overview" width="700px">
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h4>
Abstract
</h4>
<p class="text-justify" font-family: 'Didact Gothic'>
Surgical simulators provide hands-on training and learning of the necessary psychomotor skills.
Automated skill evaluation of the trainee doctors based on the video of a task being performed by
them is an important key step for the optimal utilization of such simulators. However, current skill
evaluation techniques require accurate tracking information of the instruments which restricts their
applicability to robot assisted surgeries only. In this paper, we propose a novel neural network
architecture that can perform skill evaluation using video data alone (and no tracking information).
Given the small dataset available for training such a system, the network trained using L2 regression
loss easily overfits the training data. We propose a novel rank loss to help learn robust representation,
leading to 5% improvement for skill score prediction on the benchmark JIGSAWS dataset. To demonstrate
the applicability of our method on non-robotic surgeries, we contribute a new neuro-endoscopic technical skills
(NETS) training dataset comprising of 100 short videos of 12 subjects. Our method achieved 27% improvement over
the state of the art on the NETS dataset.
</p>
</div>
</div>
<!--
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h4>
Key Results
</h4>
<div class="row" id="result_img" align="middle">
<image src="nets-data/key-result.png" class="img-responsive" alt="results" width="600px">
</div>
</div>
</div> -->
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h4>
BibTeX (Citation)
</h4>
<pre style="white-space: pre-wrap; background: hsl(220, 50%, 95%); font-size: 11px">
@inproceedings{xiang2018s3d,
title={S3d: Stacking segmental p3d for action quality assessment},
author={Baby, Britty and Chasmai, Mustafa and Banerjee, Tamajit and Suri, Ashish and Banerjee, Subhashis, and Arora, Chetan},
booktitle={2022 29th IEEE International conference on image processing (ICIP)},
pages={xxx},
year={2022},
organization={IEEE}
}
</pre>
</div>
</div>
<!--div class="row">
<div class="col-md-8 col-md-offset-2">
<h4 font-family: 'Didact Gothic'>
Acknowledgements
</h5>
<p class="text-justify" font-family: 'Didact Gothic'>
This work was supported by ???.
</p>
</div>
</div-->
<div class="row">
<div class="col-md-8 col-md-offset-2">
<p class="text-justify" font-family: 'Didact Gothic'>
<center style="font-size:10px"><b>Credits:</b> Template of this webpage from <a href="http://www.mgharbi.com/">
here.
</a></center>
</p>
</div>
</div>
</div>
</body>
</html>