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bodypartxr
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<!DOCTYPE html>
<html>
	<head>
		<link rel="stylesheet" href="file/style.css" />
		<link rel="preconnect" href="https://fonts.googleapis.com" />
		<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
		<link href="https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600;700&display=swap" rel="stylesheet" />
		<title><strong>Body Part Classification</strong></title>
	</head>
	<body>
		<div class="container">
			<h1 class="title"><strong> Body Part Classification</strong></h1>
			<h2 class="subtitle"><strong>Kalbe Digital Lab</strong></h2>
			<section class="overview">
				<div class="grid-container">
					<h3 class="overview-heading"><span class="vl">Overview</span></h3>
					<p class="overview-content">
						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.
					</p>
				</div>
				<div class="grid-container">
					<h3 class="overview-heading"><span class="vl">Dataset</span></h3>
					<div>
						<p class="overview-content">
						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>. 
						<br/>
						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.
						</p>
						<ul>
							<li>Objective: Body Part Identification</li>
							<li>Task: Classification</li>
							<li>Modality: Grayscale Images</li>
						</ul>
					</div>
				</div>
				<div class="grid-container">
					<h3 class="overview-heading"><span class="vl">Model Architecture</span></h3>
					<div>
						<p class="overview-content">
							The model architecture of ResNet18 to train x-ray images for classifying body part.
						</p>
						<img class="content-image" src="file/figures/ResNet-18.png" alt="model-architecture" width="425" height="115" style="vertical-align:middle" />
					</div>
				</div>
			</section>
			<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>
		</div>
	</body>
</html>