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<!DOCTYPE html>
<html lang="en" style="scroll-behavior: smooth">
<!-- #include file="components/head.html" -->
<script>
document.head.innerHTML = document.head.innerHTML
.replace('${title}', 'MONAI Core')
.replace('${description}', 'MONAI Core is the flagship library of Project MONAI, providing powerful capabilities for medical AI development. It features medical-specific image transforms, state-of-the-art models like UNETR for 3D segmentation, and advanced frameworks like Auto3DSeg for automated model selection.')
.replace('${canonical_url}', 'https://monai.io/core.html');
</script>
<body>
<!-- #include file="components/header.html" -->
<main class="flex-grow pt-20">
<!-- Overview Section -->
<section id="overview" class="py-24 bg-white">
<div class="container">
<div class="grid grid-cols-1 lg:grid-cols-2 gap-20 items-center">
<div class="max-w-xl">
<h1 class="text-4xl font-bold text-gray-800 mb-8 relative inline-block pb-2">
MONAI Core
<span class="absolute bottom-0 left-0 w-full h-0.5 bg-brand-primary"></span>
</h1>
<p class="text-lg text-gray-600 leading-relaxed mb-8">
MONAI Core is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm.
</p>
<div class="flex flex-wrap gap-4 mb-8">
<a href="#getting-started" class="px-6 py-2.5 rounded-lg bg-brand-primary text-white hover:bg-brand-dark transition-colors inline-flex items-center gap-2 group">
Get Started
<svg class="w-4 h-4 transition-transform group-hover:translate-x-1" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M13 7l5 5m0 0l-5 5m5-5H6"></path>
</svg>
</a>
<a href="https://docs.monai.io/en/stable/" target="_blank" rel="noopener noreferrer" class="px-6 py-2.5 rounded-lg bg-white text-brand-primary border-2 border-brand-primary hover:bg-brand-light transition-colors inline-flex items-center gap-2 group">
Documentation
<svg class="w-4 h-4 transition-transform group-hover:translate-x-1" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M13 7l5 5m0 0l-5 5m5-5H6"></path>
</svg>
</a>
<a href="wg_developers.html" class="px-6 py-2.5 rounded-lg bg-white text-brand-primary border-2 border-brand-primary hover:bg-brand-light transition-colors inline-flex items-center gap-2 group">
Get Involved
<svg class="w-4 h-4 transition-transform group-hover:translate-x-1" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M13 7l5 5m0 0l-5 5m5-5H6"></path>
</svg>
</a>
</div>
</div>
<div class="hidden lg:flex lg:justify-center">
<img src="assets/img/documentation header.png" alt="MONAI Core" class="w-full max-w-lg mx-auto">
</div>
</div>
</div>
</section>
<!-- Features Section -->
<section class="py-24 bg-brand-dark/15">
<div class="container px-4 sm:px-6 lg:px-8">
<div class="text-center max-w-4xl mx-auto mb-16">
<h2 class="text-4xl font-bold text-gray-800 mb-8 relative inline-block pb-2">
Research & Development Tools
<span class="absolute bottom-0 left-0 w-full h-0.5 bg-brand-primary"></span>
</h2>
<p class="text-xl text-gray-600">
MONAI Core provides a comprehensive toolkit that bridges research innovation and clinical application. Our tools are designed for both researchers pushing the boundaries of medical AI and clinicians seeking practical solutions.
</p>
</div>
<div class="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-8 max-w-6xl mx-auto">
<!-- Medical Transforms Card -->
<div class="bg-white p-8 border border-gray-200 rounded-lg shadow-sm hover:shadow-md transition-all duration-300 flex flex-col">
<div class="text-brand-primary mb-6">
<svg class="w-12 h-12" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M4 5a1 1 0 011-1h14a1 1 0 011 1v2a1 1 0 01-1 1H5a1 1 0 01-1-1V5zM4 13a1 1 0 011-1h6a1 1 0 011 1v6a1 1 0 01-1 1H5a1 1 0 01-1-1v-6zM16 13a1 1 0 011-1h2a1 1 0 011 1v6a1 1 0 01-1 1h-2a1 1 0 01-1-1v-6z"></path>
</svg>
</div>
<h3 class="text-xl font-bold text-gray-800 mb-4">Medical-Specific Transforms</h3>
<div class="flex-grow space-y-4">
<div class="flex items-start gap-3">
<div class="flex-shrink-0 w-6 h-6 rounded-full bg-brand-primary/10 flex items-center justify-center">
<svg class="w-4 h-4 text-brand-primary" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M5 13l4 4L19 7"></path>
</svg>
</div>
<p class="text-gray-600">Optimized for 2D, 3D, and 4D medical imaging data</p>
</div>
<div class="flex items-start gap-3">
<div class="flex-shrink-0 w-6 h-6 rounded-full bg-brand-primary/10 flex items-center justify-center">
<svg class="w-4 h-4 text-brand-primary" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M5 13l4 4L19 7"></path>
</svg>
</div>
<p class="text-gray-600">Smart caching for 10x faster data loading</p>
</div>
<div class="flex items-start gap-3">
<div class="flex-shrink-0 w-6 h-6 rounded-full bg-brand-primary/10 flex items-center justify-center">
<svg class="w-4 h-4 text-brand-primary" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M5 13l4 4L19 7"></path>
</svg>
</div>
<p class="text-gray-600">Reproducible pipelines with deterministic training</p>
</div>
<div class="flex items-start gap-3">
<div class="flex-shrink-0 w-6 h-6 rounded-full bg-brand-primary/10 flex items-center justify-center">
<svg class="w-4 h-4 text-brand-primary" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M5 13l4 4L19 7"></path>
</svg>
</div>
<p class="text-gray-600">DICOM, NIFTI, and PNG/JPEG support built-in</p>
</div>
</div>
</div>
<!-- State-of-the-Art Models Card -->
<div class="bg-white p-8 border border-gray-200 rounded-lg shadow-sm hover:shadow-md transition-all duration-300 flex flex-col">
<div class="text-brand-primary mb-6">
<svg class="w-12 h-12" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9 3v2m6-2v2M9 19v2m6-2v2M5 9H3m2 6H3m18-6h-2m2 6h-2M7 19h10a2 2 0 002-2V7a2 2 0 00-2-2H7a2 2 0 00-2 2v10a2 2 0 002 2zM9 9h6v6H9V9z"></path>
</svg>
</div>
<h3 class="text-xl font-bold text-gray-800 mb-4">State-of-the-Art Models</h3>
<div class="flex-grow space-y-4">
<div class="flex items-start gap-3">
<div class="flex-shrink-0 w-6 h-6 rounded-full bg-brand-primary/10 flex items-center justify-center">
<svg class="w-4 h-4 text-brand-primary" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M5 13l4 4L19 7"></path>
</svg>
</div>
<p class="text-gray-600">VISTA-3D: Leading 3D segmentation model</p>
</div>
<div class="flex items-start gap-3">
<div class="flex-shrink-0 w-6 h-6 rounded-full bg-brand-primary/10 flex items-center justify-center">
<svg class="w-4 h-4 text-brand-primary" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M5 13l4 4L19 7"></path>
</svg>
</div>
<p class="text-gray-600">31+ pre-trained models ready for fine-tuning</p>
</div>
<div class="flex items-start gap-3">
<div class="flex-shrink-0 w-6 h-6 rounded-full bg-brand-primary/10 flex items-center justify-center">
<svg class="w-4 h-4 text-brand-primary" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M5 13l4 4L19 7"></path>
</svg>
</div>
<p class="text-gray-600">Easy model customization and extension</p>
</div>
<div class="flex items-start gap-3">
<div class="flex-shrink-0 w-6 h-6 rounded-full bg-brand-primary/10 flex items-center justify-center">
<svg class="w-4 h-4 text-brand-primary" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M5 13l4 4L19 7"></path>
</svg>
</div>
<p class="text-gray-600">Automated model selection with Auto3DSeg</p>
</div>
</div>
</div>
<!-- Advanced Frameworks Card -->
<div class="bg-white p-8 border border-gray-200 rounded-lg shadow-sm hover:shadow-md transition-all duration-300 flex flex-col">
<div class="text-brand-primary mb-6">
<svg class="w-12 h-12" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M19 11H5m14 0a2 2 0 012 2v6a2 2 0 01-2 2H5a2 2 0 01-2-2v-6a2 2 0 012-2m14 0V9a2 2 0 00-2-2M5 11V9a2 2 0 012-2m0 0V5a2 2 0 012-2h6a2 2 0 012 2v2M7 7h10"></path>
</svg>
</div>
<h3 class="text-xl font-bold text-gray-800 mb-4">Research Workflows</h3>
<div class="flex-grow space-y-4">
<div class="flex items-start gap-3">
<div class="flex-shrink-0 w-6 h-6 rounded-full bg-brand-primary/10 flex items-center justify-center">
<svg class="w-4 h-4 text-brand-primary" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M5 13l4 4L19 7"></path>
</svg>
</div>
<p class="text-gray-600">Experiment tracking and version control</p>
</div>
<div class="flex items-start gap-3">
<div class="flex-shrink-0 w-6 h-6 rounded-full bg-brand-primary/10 flex items-center justify-center">
<svg class="w-4 h-4 text-brand-primary" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M5 13l4 4L19 7"></path>
</svg>
</div>
<p class="text-gray-600">Real-time visualization with TensorBoard</p>
</div>
<div class="flex items-start gap-3">
<div class="flex-shrink-0 w-6 h-6 rounded-full bg-brand-primary/10 flex items-center justify-center">
<svg class="w-4 h-4 text-brand-primary" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M5 13l4 4L19 7"></path>
</svg>
</div>
<p class="text-gray-600">Multi-GPU training support built-in</p>
</div>
<div class="flex items-start gap-3">
<div class="flex-shrink-0 w-6 h-6 rounded-full bg-brand-primary/10 flex items-center justify-center">
<svg class="w-4 h-4 text-brand-primary" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M5 13l4 4L19 7"></path>
</svg>
</div>
<p class="text-gray-600">Automated hyperparameter tuning</p>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Components Section -->
<section id="getting-started" class="py-24 bg-white">
<div class="container">
<div class="text-center max-w-4xl mx-auto mb-16">
<h2 class="text-4xl font-bold text-gray-800 mb-8 relative inline-block pb-2">
Modular Components
<span class="absolute bottom-0 left-0 w-full h-0.5 bg-brand-primary"></span>
</h2>
<p class="text-xl text-gray-600">
Built on PyTorch, MONAI Core provides composable transforms, standardized interfaces, and validated implementations. Import only what you need or use our end-to-end workflows.
</p>
</div>
<div class="grid grid-cols-1 lg:grid-cols-2 gap-16 items-start">
<div>
<div class="bg-white p-8 border border-gray-200 rounded-lg shadow-sm">
<h3 class="text-xl font-bold text-gray-800 mb-6">Core Components</h3>
<div class="grid grid-cols-2 gap-6">
<div class="space-y-4">
<a href="https://docs.monai.io/en/stable/metrics.html" target="_blank" class="block p-4 bg-gray-50 hover:bg-gray-100 rounded-lg transition-colors group">
<div class="flex items-center justify-between">
<div>
<h3 class="font-semibold text-gray-800 group-hover:text-brand-primary">Metrics</h3>
<p class="text-sm text-gray-600 mt-1">Specialized metrics for medical image analysis (Dice, Hausdorff, etc.)</p>
</div>
<svg class="w-4 h-4 text-gray-400 group-hover:text-brand-primary transition-transform group-hover:translate-x-1" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9 5l7 7-7 7"></path>
</svg>
</div>
</a>
<a href="https://docs.monai.io/en/stable/losses.html" target="_blank" class="block p-4 bg-gray-50 hover:bg-gray-100 rounded-lg transition-colors group">
<div class="flex items-center justify-between">
<div>
<h3 class="font-semibold text-gray-800 group-hover:text-brand-primary">Losses</h3>
<p class="text-sm text-gray-600 mt-1">Medical-specific loss functions for segmentation, registration, and reconstruction</p>
</div>
<svg class="w-4 h-4 text-gray-400 group-hover:text-brand-primary transition-transform group-hover:translate-x-1" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9 5l7 7-7 7"></path>
</svg>
</div>
</a>
<a href="https://docs.monai.io/en/stable/networks.html" target="_blank" class="block p-4 bg-gray-50 hover:bg-gray-100 rounded-lg transition-colors group">
<div class="flex items-center justify-between">
<div>
<h3 class="font-semibold text-gray-800 group-hover:text-brand-primary">Networks</h3>
<p class="text-sm text-gray-600 mt-1">State-of-the-art medical imaging architectures</p>
</div>
<svg class="w-4 h-4 text-gray-400 group-hover:text-brand-primary transition-transform group-hover:translate-x-1" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9 5l7 7-7 7"></path>
</svg>
</div>
</a>
<a href="https://docs.monai.io/en/stable/apps.html" target="_blank" class="block p-4 bg-gray-50 hover:bg-gray-100 rounded-lg transition-colors group">
<div class="flex items-center justify-between">
<div>
<h3 class="font-semibold text-gray-800 group-hover:text-brand-primary">Applications</h3>
<p class="text-sm text-gray-600 mt-1">End-to-end applications</p>
</div>
<svg class="w-4 h-4 text-gray-400 group-hover:text-brand-primary transition-transform group-hover:translate-x-1" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9 5l7 7-7 7"></path>
</svg>
</div>
</a>
<a href="https://docs.monai.io/en/stable/data.html" target="_blank" class="block p-4 bg-gray-50 hover:bg-gray-100 rounded-lg transition-colors group">
<div class="flex items-center justify-between">
<div>
<h3 class="font-semibold text-gray-800 group-hover:text-brand-primary">Datasets</h3>
<p class="text-sm text-gray-600 mt-1">Efficient medical imaging data loading and processing</p>
</div>
<svg class="w-4 h-4 text-gray-400 group-hover:text-brand-primary transition-transform group-hover:translate-x-1" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9 5l7 7-7 7"></path>
</svg>
</div>
</a>
</div>
<div class="space-y-4">
<a href="https://docs.monai.io/en/stable/transforms.html" target="_blank" class="block p-4 bg-gray-50 hover:bg-gray-100 rounded-lg transition-colors group">
<div class="flex items-center justify-between">
<div>
<h3 class="font-semibold text-gray-800 group-hover:text-brand-primary">Transforms</h3>
<p class="text-sm text-gray-600 mt-1">Domain-specific medical image transformations</p>
</div>
<svg class="w-4 h-4 text-gray-400 group-hover:text-brand-primary transition-transform group-hover:translate-x-1" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9 5l7 7-7 7"></path>
</svg>
</div>
</a>
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<h3 class="font-semibold text-gray-800 group-hover:text-brand-primary">Optimizers</h3>
<p class="text-sm text-gray-600 mt-1">Deep learning optimizers for medical tasks</p>
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<p class="text-sm text-gray-600 mt-1">2D/3D medical image visualization tools</p>
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<code class="block bg-gray-50 rounded-lg p-4 font-mono text-sm text-gray-700 whitespace-pre">from monai.transforms import (
Compose,
LoadImage,
ScaleIntensity,
AddChannel
)
# Define transforms for image preprocessing
transforms = Compose([
LoadImage(image_only=True),
AddChannel(),
ScaleIntensity()
])
# Apply transforms to your image
image = transforms(image_path)</code>
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Auto3DSeg: State-of-the-Art Medical Segmentation
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</h2>
<p class="text-xl text-gray-600">
Achieve hassle-free, state-of-the-art performance in 3D medical image segmentation with MONAI's automated solution for both novice and expert users.
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<h4 class="font-semibold text-gray-800">Dataset Analysis</h4>
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<p class="text-gray-600">Built-in GPU support for accelerated training, validation, and inference.</p>
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<h4 class="font-semibold text-gray-800">Model Ensemble</h4>
<p class="text-gray-600">Integration of multiple models for enhanced accuracy and reliability.</p>
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<h3 class="text-2xl font-bold text-gray-800 mb-6">Competition Achievements</h3>
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<h4 class="font-semibold text-gray-800 mb-2">MICCAI 2023 Challenges</h4>
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Multiple 1st Place wins in BraTS 2023
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1st Place in KiTS 2023 (Kidney Segmentation)
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1st Place in SEG.A. 2023 (Aorta Segmentation)
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1st Place in MVSEG 2023 (Mitral Valve)
</li>
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Research Impact
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MONAI Core is trusted by researchers worldwide, powering breakthrough discoveries in medical AI.
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<h3 class="text-xl font-bold text-gray-800 mb-4">Research Publications</h3>
<p class="text-gray-600 mb-6">Over 1500 peer-reviewed papers have been published using MONAI, advancing the field of medical AI.</p>
<div class="bg-gray-50 p-4 rounded-lg">
<div class="text-4xl font-bold text-brand-primary mb-2">1500+</div>
<div class="text-sm text-gray-600">Published Papers</div>
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<h3 class="text-xl font-bold text-gray-800 mb-4">Competition Success</h3>
<p class="text-gray-600 mb-6">MONAI-powered solutions have won numerous medical imaging challenges and competitions.</p>
<div class="bg-gray-50 p-4 rounded-lg">
<div class="text-4xl font-bold text-brand-primary mb-2">17</div>
<div class="text-sm text-gray-600">Challenge Wins</div>
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<h3 class="text-xl font-bold text-gray-800 mb-4">Pre-trained Models</h3>
<p class="text-gray-600 mb-6">Access a growing collection of pre-trained models for various medical imaging tasks.</p>
<div class="bg-gray-50 p-4 rounded-lg">
<div class="text-4xl font-bold text-brand-primary mb-2">31</div>
<div class="text-sm text-gray-600">Model Zoo Models</div>
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How to Cite MONAI Core
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</h2>
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If you use MONAI in your research, please cite our paper:
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<pre class="bg-gray-50 rounded-lg p-4 font-mono text-sm text-gray-700 whitespace-pre-wrap">@article{cardoso2022monai,
title={MONAI: An open-source framework for deep learning in healthcare},
author={M Jorge Cardoso, Wenqi Li, Richard Brown, Nic Ma, Eric Kerfoot, Yiheng Wang, Benjamin Murrey, Andriy Myronenko, Can Zhao, Dong Yang, Vishwesh Nath, Yufan He, Ziyue Xu, Ali Hatamizadeh, Andriy Myronenko, Wentao Zhu, Yun Liu, Mingxin Zheng, Yucheng Tang, Isaac Yang, Michael Zephyr, Behrooz Hashemian, Sachidanand Alle, Mohammad Zalbagi Darestani, Charlie Budd, Marc Modat, Tom Vercauteren, Guotai Wang, Yiwen Li, Yipeng Hu, Yunguan Fu, Benjamin Gorman, Hans Johnson, Brad Genereaux, Barbaros S Erdal, Vikash Gupta, Andres Diaz-Pinto, Andre Dourson, Lena Maier-Hein, Paul F Jaeger, Michael Baumgartner, Jayashree Kalpathy-Cramer, Mona Flores, Justin Kirby, Lee A D Cooper, Holger R Roth, Daguang Xu, David Bericat, Ralf Floca, S Kevin Zhou, Haris Shuaib, Keyvan Farahani, Klaus H Maier-Hein, Stephen Aylward, Prerna Dogra, Sebastien Ourselin, Andrew Feng},
journal={arXiv:2211.02701},
year={2022}
}</pre>
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<h3 class="text-xl font-bold text-gray-800 mb-4">Tutorials</h3>
<p class="text-gray-600">Learn from hands-on tutorials and examples covering a wide range of medical imaging tasks.</p>
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