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<!DOCTYPE html><!-- Author: Pranav Rajpurkar 2017-->
<html>

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    <title>Object-CXR: Automatic detection of  foreign objects on chest X-rays</title>
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          content="CheXpert is a large dataset of chest x-rays and competition for automated chest x-ray interpretation, which features uncertainty labels and radiologist-labeled reference standard evaluation sets.">
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          content="Object-CXR: Automatic detection of  foreign objects on chest X-rays.">
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            <div class="navbar-header"><a class="navbar-brand page-scroll"
                   href="/">JF HEALTHCARE</a></div>
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            <div class="row">
                <div class="col-lg-12"><img id="title-image"
                         src="/competitions/chexpert/img/logo.svg">
                    <h3 id="page-subtitle">Automatic detection of  foreign objects on chest X-rays</h3>
                </div>
            </div>
        </div>
    </section>
    <section>
        <div class="container">
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                <div class="col-md-7">
                    <h1>Introduction</h1>
                    <p>Analyzing chest X-rays is a common clinical approach for diagnosing pulmonary and heart diseases. However, foreign objects are occasionally presented on chest X-ray images, especially in rural and remote locations where standard filming guidances are not strictly followed. Foreign objects on chest X-rays may obscure pathological finds, thus increasing false negative diagnosis. They may also confuse junior radiologists from real pathological findings, e.g. buttons are visually similar to nodules on chest X-ray, thus increasing false positive diagnosis. Therefore, automatically detecting foreign objects on chest X-ray is important and may potentially improve overall diagnosis accuracy, e.g. by suggesting re-filming in the telemedicine setting.
                         </p><a class="btn btn-default"
                       href="http://www.jfhealthcare.com/">View JF HEALTHCARE</a>
                    <h2>Background</h2>
                    <p> Detecting foreign objects is particularly challenging for deep learning (DL) based systems, as specific types of objects presented in the test set may be rarely or never seen in the training set, thus posing a few-shot/zero-shot learning problem. We hope this open dataset and challenge could both help the development of automatic foreign objects detection system, and promote the general research of object detection on chest X-rays, as large scale chest X-ray datasets with strong annotations are limited to the best of our knowledge.</p>
                </div>
                <div class="col-md-5">
                    <h2>Leaderboard</h2>
                    <p> Will your model perform as well as radiologists in detecting different pathologies in chest
                        X-rays?</p>
                    <table class="table performanceTable">
                        <tr>
                            <th>Rank</th>
                            <th>Date</th>
                            <th>Model</th>
                            <th>AUC</th>
                            <th>Num Rads Below Curve</th>
                        </tr>
                        <tr>
                            <td class="rank">1 <br></td>
                            <td><span class="date label label-default">Sep 01, 2019</span></td>
                            <td style="word-break:break-word;">Qitingshe<em> JF
                                    Institute </em></td>
                            <td><b>0.930</b></td>
                            <td>2.6</td>
                        </tr>
                        <tr>
                            <td class="rank">2 <br></td>
                            <td><span class="date label label-default">Oct 15, 2019</span></td>
                            <td style="word-break:break-word;">Deadpoppy<em> ensemble </em></td>
                            <td>0.929</td>
                            <td>2.6</td>
                        </tr>
                    </table>
                    <h3>How can I participate?</h3>
                    <p>Object-CXR uses a hidden test set for official evaluation of models. Teams submit their executable
                        code on Codalab, which is then run on a test set that is not publicly readable. Such a setup
                        preserves the integrity of the test results.</p>
                    <p>Here's a tutorial walking you through official evaluation of your model. Once your model has been
                        evaluated officially, your scores will be added to the leaderboard.</p>
                    <ul class="list-inline">
                        <li><a class="btn btn-lg btn-default"
                               href="https://worksheets.codalab.org/worksheets/0xcd2fb3db8ae74d03b53ad4c5bf81ebe2">Submission
                                Tutorial</a></li>
                        <li></li>
                    </ul>
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                    <h2>OverView</h2>
                    <p>We provide a large dataset of chest X-rays with strong annotations of foreign objects, and the competition for automatic detection of foreign objects. Specifically, 5000 frontal chest X-ray images with foreign objects presented and 5000 frontal chest X-ray images without foreign objects are provided. All the chest X-ray images were filmed in township hospitals in China and collected through our telemedicine platform. Foreign objects within the lung field of each chest X-ray are annotated with bounding boxes, ellipses or masks depending on the shape of the objects.</p>

                    <h3>Dataset</h3>
                    <p>5000 frontal chest X-ray images with foreign objects presented and 5000 frontal chest X-ray images without foreign objects were filmed and collected from about 300 township hosiptials in China. 12 medically-trained radiologists with 1 to 3 years of experience annotated all the images. Each annotator manually annotates the potential foreign objects on a given chest X-ray presented within the lung field. Foreign objects were annotated with bounding boxes, bounding ellipses or masks depending on the shape of the objects. Support devices were excluded from annotation. A typical frontal chest X-ray with foreign objects annotated looks like this:</p>

                    <ul>
                        <li><b>training</b>: 4000 chest X-rays with foreign objects presented; 4000 chest X-rays without foreign objects.</li>
                        <li><b>validation</b>: 500 chest X-rays with foreign objects presented; 500 chest X-rays without foreign objects.</li>
                        <li><b>test</b>: 500 chest X-rays with foreign objects presented; 500 chest X-rays without foreign objects.</li>
                    </ul>
                    <p>We randomly split the 10000 images into training, validation and test dataset.</p>
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