bodypartxr / index.html
Jason Adrian
Changing the image architecture + credits
8477e84
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<title><strong>Body Part Classification</strong></title>
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<h1 class="title"><strong> Body Part Classification</strong></h1>
<h2 class="subtitle"><strong>Kalbe Digital Lab</strong></h2>
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<h3 class="overview-heading"><span class="vl">Overview</span></h3>
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The Body Part Classification program serves the critical purpose of categorizing body parts from DICOM x-ray scans into five distinct classes: abdominal, adult chest, pediatric chest, spine, and others. This program trained using ResNet18 model.
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<h3 class="overview-heading"><span class="vl">Dataset</span></h3>
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The program has been meticulously trained on a robust and diverse dataset, specifically <a href="https://vindr.ai/datasets/bodypartxr" target="_blank">VinDrBodyPartXR Dataset.</a>.
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This dataset is introduced by Vingroup of Big Data Institute which include 16,093 x-ray images that are collected and manually annotated. It is a highly valuable resource that has been instrumental in the training of our model.
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<li>Objective: Body Part Identification</li>
<li>Task: Classification</li>
<li>Modality: Grayscale Images</li>
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<h3 class="overview-heading"><span class="vl">Model Architecture</span></h3>
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The model architecture of ResNet18 to train x-ray images for classifying body part.
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<img class="content-image" src="file/figures/ResNet-18.png" alt="model-architecture" width="425" height="115" style="vertical-align:middle" />
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<h3 class="overview-heading"><span class="vl">Demo</span></h3>
<p class="overview-content">Please select or upload a body part x-ray scan image to see the capabilities of body part classification with this model</p>
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