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<!DOCTYPE html>
<html lang="en" class="scroll-smooth">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>BIC-MAC Challenge | MICCAI 2026</title>
<meta name="description" content="Big Cross-Modal Attenuation Correction Challenge - A MICCAI Challenge">
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<a href="#" class="text-xl font-bold gradient-text">BIC-MAC</a>
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<a href="#about" class="nav-link text-gray-600 hover:text-gray-900 text-sm font-medium">About</a>
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Register Now
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<h1 class="text-5xl md:text-7xl font-bold mb-6 leading-tight">
Big Cross-Modal<br>
<span class="gradient-text">Attenuation Correction</span><br>
Challenge
</h1>
<p class="text-xl md:text-2xl text-gray-700 mb-12 max-w-3xl mx-auto">
Synthesize pseudo-CT from NAC-PET, MRI, and 2D Topogram to enable CT-less reconstruction of PET images.
</p>
<div class="flex flex-col sm:flex-row gap-4 justify-center mb-12">
<a href="#participate" class="bg-gradient-to-r from-blue-600 to-purple-600 text-white px-8 py-4 rounded-lg font-medium hover:shadow-xl transition-shadow text-lg">
Register Now
</a>
<a href="https://github.com/bic-mac-challenge/challenge-codebase" target="_blank" rel="noopener noreferrer" class="bg-white/80 backdrop-blur-sm border-2 border-gray-300 text-gray-700 px-8 py-4 rounded-lg font-medium hover:bg-white hover:border-gray-400 transition-all text-lg inline-flex items-center justify-center">
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</svg>
View on GitHub
</a>
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View on Codabench
</a>
</div>
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<img src="images/organizations/rigshospitalet.png" alt="Rigshospitalet" class="h-16 w-auto object-contain opacity-80">
<img src="images/organizations/depict.jpg" alt="DEPICT" class="h-16 w-auto object-contain opacity-80">
<img src="images/organizations/dtu-logo.png" alt="Technical University of Denmark" class="h-16 w-auto object-contain opacity-80">
<img src="images/organizations/ku_logo_dk_hh.png" alt="University of Copenhagen" class="h-16 w-auto object-contain opacity-80">
<img src="images/organizations/ku_leuven.png" alt="KU Leuven" class="h-16 w-auto object-contain opacity-80">
<img src="images/organizations/qim.png" alt="QIM" class="h-16 w-auto object-contain opacity-80">
<img src="images/organizations/synerbi.png" alt="SyneRBI" class="h-16 w-auto object-contain opacity-80">
</div>
</div>
</div>
</div>
</section>
<!-- About Section -->
<section id="about" class="py-20 px-6 lg:px-8 bg-gray-50">
<div class="max-w-7xl mx-auto">
<div class="text-center mb-16">
<h2 class="text-4xl md:text-5xl font-bold mb-6">About the Challenge</h2>
<p class="text-xl text-gray-600 max-w-4xl mx-auto leading-relaxed">
To reconstruct an accurate total-body PET image, you need a CT scan to correct for photons being
attenuated by dense tissue like bone. But CT adds radiation — undesirable for dose-sensitive
populations like pediatric or obstetric patients — and combined PET/MRI scanners have no CT at all.
In both scenarios, we need a robust generative algorithm that can synthesize CTs.
</p>
</div>
<!-- Challenge Introduction -->
<div class="bg-white rounded-2xl p-8 md:p-12 shadow-sm mb-16">
<div class="max-w-4xl mx-auto">
<h3 class="text-2xl font-bold mb-8 text-center">Your Task</h3>
<p class="text-lg text-gray-700 leading-relaxed mb-6">
Build a generative AI model that synthesizes a <span class="font-semibold text-blue-600">3D pseudo-CT</span> from
whole-body MRI, non-attenuation-corrected PET (NAC-PET), and/or 2D X-ray-like Topograms.
<span class="font-semibold text-blue-600">No knowledge of PET reconstruction is required to compete.</span>
The challenge is simply an image-to-image prediction task, and the reconstruction evaluation is handled for you.
</p>
<p class="text-lg text-gray-700 leading-relaxed">
You'll work with a novel, <span class="font-semibold text-purple-600">age- and sex-balanced dataset</span> of
total-body PET/CT/MR images and PET sinograms from 99 healthy volunteers, along with open-source
<a href="https://stir.sourceforge.net/" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:underline">STIR</a> reconstruction
containers — enabling closed-loop PET reconstruction accessible to anyone with Docker.
</p>
</div>
</div>
<div class="bg-gradient-to-br from-blue-50 to-purple-50 rounded-xl p-2 md:p-8 flex items-center justify-center min-h-[400px] mb-16">
<img
src="images/illustration.png"
alt="Diagram showing modalities workflow"
class="max-w-full md:max-w-3xl h-auto rounded-lg shadow-sm mix-blend-multiply"
>
</div>
<!-- Key Features Heading -->
<div class="text-center mb-12">
<h3 class="text-3xl font-bold">What Makes This Challenge Unique?</h3>
</div>
<div class="grid md:grid-cols-3 gap-8 mb-16">
<!-- Feature 1 -->
<div class="bg-white p-8 rounded-2xl shadow-sm hover:shadow-md transition-shadow border-t-4 border-blue-600">
<div class="w-16 h-16 bg-blue-100 rounded-xl flex items-center justify-center mb-6">
<svg class="w-8 h-8 text-blue-600" 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 mb-4">Multimodal Fusion</h3>
<p class="text-gray-600 leading-relaxed">
Tackle the challenge of fusing spatially unaligned information from 3D MRI with 3D NAC-PET and 2D Topograms to produce a coherent 3D pseudo-CT.
</p>
</div>
<!-- Feature 2 -->
<div class="bg-white p-8 rounded-2xl shadow-sm hover:shadow-md transition-shadow border-t-4 border-purple-600">
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<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M4 4v5h.582m15.356 2A8.001 8.001 0 004.582 9m0 0H9m11 11v-5h-.581m0 0a8.003 8.003 0 01-15.357-2m15.357 2H15"></path>
</svg>
</div>
<h3 class="text-xl font-bold mb-4">Open-Source Reconstruction</h3>
<p class="text-gray-600 leading-relaxed">
Evaluate the attenuation correction effect of your predicted CTs directly with local PET reconstruction via <a href="https://stir.sourceforge.net/" target="_blank" rel="noopener noreferrer" class="text-blue-600 hover:underline">STIR</a> — no proprietary vendor software required.
</p>
</div>
<!-- Feature 3 -->
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<div class="w-16 h-16 bg-pink-100 rounded-xl flex items-center justify-center mb-6">
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<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9 12l2 2 4-4m5.618-4.016A11.955 11.955 0 0112 2.944a11.955 11.955 0 01-8.618 3.04A12.02 12.02 0 003 9c0 5.591 3.824 10.29 9 11.622 5.176-1.332 9-6.03 9-11.622 0-1.042-.133-2.052-.382-3.016z"></path>
</svg>
</div>
<h3 class="text-xl font-bold mb-4">Novel Dataset</h3>
<p class="text-gray-600 leading-relaxed">
Access a unique, age- and sex-balanced dataset with total-body PET/CT/MR images and PET sinograms from 99 healthy volunteers — purpose-built for this challenge.
</p>
</div>
</div>
<!-- Overview -->
<div class="bg-white rounded-2xl p-8 md:p-12 shadow-sm">
<h3 class="text-2xl font-bold mb-8 text-center">Challenge Abstract</h3>
<div class="max-w-none text-gray-600">
<p class="text-lg leading-relaxed mb-6">
The advent of Long Axial Field-of-View (LAFOV) PET scanners has shifted the dosimetry paradigm in PET/CT
imaging. The high sensitivity of these systems allows for substantial reductions in radiotracer activity, rendering
the volumetric CT component the dominant source of ionizing radiation. For dose-sensitive populations such as
pediatric and obstetric cohorts, eliminating the volumetric CT entirely is highly desirable. However, the CT serves
a dual purpose: providing anatomical context and enabling attenuation correction (AC) for PET reconstruction,
as the attenuation map is typically derived directly from the CT. Similarly, whole-body studies acquired on
PET/MRI systems require estimation of the attenuation map from MR images. In both scenarios, the absence of
a CT poses a reconstruction challenge.
</p>
<p class="text-lg leading-relaxed mb-6">
To address this, the Big Cross-Modal Attenuation Correction (BIC-MAC) challenge tasks participants with
synthesizing a 3D pseudo-CT from other available modalities. We present a novel multimodal dataset comprising
whole-body PET, CT, Topogram (scout radiograph), and MRI for 99 healthy volunteers. The cohort is age- and
sex-stratified, with data acquired on Siemens Biograph Vision Quadra and Siemens MAGNETOM Vida
scanners. Participants will receive a training set of 75 cases containing Non-Attenuation Corrected (NAC)
[18F]FDG PET images, scan-planning Topograms, and same-day DIXON MRI, alongside reference CT and CT-based attenuation-corrected PET (CTAC-PET) images. Critically, we also provide scatter maps, sinograms, and
Docker containers with open-source reconstruction software, enabling closed-loop optimization on the training
set - a capability previously restricted to hospital sites with access to proprietary vendor software.
</p>
<p class="text-lg leading-relaxed">
The challenge comprises a single task: generate a pseudo-CT from the available input modalities. The pseudo-CT will be used to reconstruct PET images, which are then quantitatively compared against reference CTAC-PET images. Both static and dynamic PET reconstructions are evaluated to assess downstream accuracy across
different clinical contexts. A defining technical characteristic of this challenge is the integration of modalities with
different dimensionalities and acquisition geometries. While the 3D NAC-PET and 2D Topograms are spatially
aligned with the target attenuation map, both lack anatomical detail. In contrast, whole-body MRI offers high
bone and soft-tissue contrast but is acquired in a different scanner geometry with different patient positioning
and body deformations. Consequently, participants must develop algorithms capable of fusing spatially
unaligned information from 3D volumetric MRI with that of the 3D NAC-PET and 2D Topograms.
</p>
</div>
</div>
</div>
</section>
<!-- Timeline Section -->
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<h2 class="text-4xl md:text-5xl font-bold mb-6">Important Dates</h2>
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</p>
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<h3 class="text-base font-bold">Registration Opens</h3>
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</div>
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April 1, 2026
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<h3 class="text-base font-bold">Challenge starts (data released)</h3>
<p class="text-sm text-gray-600">Training (n=75) and validation (n=4) cases released on Hugging Face</p>
</div>
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April 8, 2026
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<h3 class="text-base font-bold">Pre-evaluation Period Opens</h3>
<p class="text-sm text-gray-600">Teams can submit for pre-evaluation on validation data</p>
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May 15, 2026
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<h3 class="text-base font-bold">Final Evaluation Period Opens</h3>
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June 15, 2026
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<h3 class="text-base font-bold">Registration Closes</h3>
<p class="text-sm text-gray-600">Final deadline for team registration</p>
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August 15, 2026
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<h3 class="text-base font-bold">Submission Deadline</h3>
<p class="text-sm text-gray-600">Pre-evaluation and final evaluation close</p>
</div>
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<span class="inline-block bg-orange-50 text-orange-700 px-3 py-1 rounded-lg text-sm font-semibold">
August 15, 2026
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<h3 class="text-base font-bold">Winners Announced</h3>
<p class="text-sm text-blue-100">CodaBench leaderboard updated with test scores; top three winners announced</p>
</div>
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<span class="inline-block bg-white/20 backdrop-blur-sm px-3 py-1 rounded-lg text-sm font-semibold">
September 1, 2026
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</div>
</div>
</div>
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</section>
<!-- Organizers Section -->
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<h2 class="text-4xl md:text-5xl font-bold mb-2">Organizing Committee</h2>
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Meet the team behind the challenge
</p>
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<!-- Organizing Committee -->
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<img src="images/organizers/Christian_person.png" alt="Christian Hinge" class="w-full h-full object-cover">
</div>
<h4 class="font-bold text-sm mb-0.5">Christian Hinge</h4>
<p class="text-gray-600 text-xs mb-0.5">PhD Student, Rigshospitalet</p>
<p class="text-gray-500 text-xs">Lead Organizer</p>
</div>
<div class="text-center">
<div class="w-20 h-20 rounded-full mx-auto mb-2 overflow-hidden">
<img src="images/organizers/Claes_person.png" alt="Claes Nøhr Ladefoged" class="w-full h-full object-cover">
</div>
<h4 class="font-bold text-sm mb-0.5">Claes Nøhr Ladefoged</h4>
<p class="text-gray-600 text-xs mb-0.5">Associate Professor, Technical University of Denmark</p>
<p class="text-gray-500 text-xs">Co-Organizer</p>
</div>
<div class="text-center">
<div class="w-20 h-20 rounded-full mx-auto mb-2 overflow-hidden">
<img src="images/organizers/Flemming_person.png" alt="Flemming Littrup Andersen" class="w-full h-full object-cover">
</div>
<h4 class="font-bold text-sm mb-0.5">Flemming Littrup Andersen</h4>
<p class="text-gray-600 text-xs mb-0.5">Professor, Rigshospitalet</p>
<p class="text-gray-500 text-xs">Co-Organizer</p>
</div>
<div class="text-center">
<div class="w-20 h-20 rounded-full mx-auto mb-2 overflow-hidden">
<img src="images/organizers/Georg_person.jpg" alt="Georg Schramm" class="w-full h-full object-cover">
</div>
<h4 class="font-bold text-sm mb-0.5">Georg Schramm</h4>
<p class="text-gray-600 text-xs mb-0.5">Assistant Professor, KU Leuven</p>
<p class="text-gray-500 text-xs">Co-Organizer</p>
</div>
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<div class="w-20 h-20 rounded-full mx-auto mb-2 overflow-hidden">
<img src="images/organizers/vaibhav.jpeg" alt="Vaibhav Bahel" class="w-full object-cover" style="transform: scale(1.4) translateY(20px);">
</div>
<h4 class="font-bold text-sm mb-0.5">Vaibhav Bahel</h4>
<p class="text-gray-600 text-xs mb-0.5">Student, Technical University of Denmark</p>
<p class="text-gray-500 text-xs">Co-Organizer</p>
</div>
<div class="text-center">
<div class="w-20 h-20 rounded-full mx-auto mb-2 overflow-hidden">
<img src="images/organizers/Nanna_person.png" alt="Nanna Overbeck Petersen" class="w-full h-full object-cover">
</div>
<h4 class="font-bold text-sm mb-0.5">Nanna Overbeck Petersen</h4>
<p class="text-gray-600 text-xs mb-0.5">PhD Student, Rigshospitalet</p>
<p class="text-gray-500 text-xs">Co-Organizer</p>
</div>
<div class="text-center">
<div class="w-20 h-20 rounded-full mx-auto mb-2 overflow-hidden">
<img src="images/organizers/kris.jpeg" alt="Kris Thielemans" class="w-full h-full object-cover">
</div>
<h4 class="font-bold text-sm mb-0.5">Kris Thielemans</h4>
<p class="text-gray-600 text-xs mb-0.5">Professor, University College London</p>
<p class="text-gray-500 text-xs">STIR Team</p>
</div>
<div class="text-center">
<div class="w-20 h-20 rounded-full mx-auto mb-2 overflow-hidden">
<img src="images/organizers/tsoumpas.webp" alt="Charalampos Tsoumpas" class="w-full h-full object-cover">
</div>
<h4 class="font-bold text-sm mb-0.5">Charalampos Tsoumpas</h4>
<p class="text-gray-600 text-xs mb-0.5">Professor, University of Groningen</p>
<p class="text-gray-500 text-xs">STIR Team</p>
</div>
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<div class="w-20 h-20 rounded-full mx-auto mb-2 overflow-hidden bg-gray-200 flex items-center justify-center">
<svg class="w-16 h-16 text-gray-400" fill="currentColor" viewBox="0 0 24 24"><path d="M12 12c2.7 0 4.8-2.1 4.8-4.8S14.7 2.4 12 2.4 7.2 4.5 7.2 7.2 9.3 12 12 12zm0 2.4c-3.2 0-9.6 1.6-9.6 4.8v2.4h19.2v-2.4c0-3.2-6.4-4.8-9.6-4.8z"/></svg>
</div>
<h4 class="font-bold text-sm mb-0.5">Zekai Li</h4>
<p class="text-gray-600 text-xs mb-0.5">PhD Candidate, University of Groningen</p>
<p class="text-gray-500 text-xs">STIR Team</p>
</div>
<div class="text-center">
<div class="w-20 h-20 rounded-full mx-auto mb-2 overflow-hidden">
<img src="images/organizers/nikos.jpeg" alt="Nikos Efthymiou" class="w-full h-full object-cover">
</div>
<h4 class="font-bold text-sm mb-0.5">Nikos Efthymiou</h4>
<p class="text-gray-600 text-xs mb-0.5">Senior Scientist, University of Groningen</p>
<p class="text-gray-500 text-xs">STIR Team</p>
</div>
<div class="text-center">
<div class="w-20 h-20 rounded-full mx-auto mb-2 overflow-hidden">
<img src="images/organizers/felipe.webp" alt="Felipe Delestro Matos" class="w-full h-full object-cover">
</div>
<h4 class="font-bold text-sm mb-0.5">Felipe Delestro Matos</h4>
<p class="text-gray-600 text-xs mb-0.5">Senior Researcher, Technical University of Denmark</p>
<p class="text-gray-500 text-xs">Infrastructure Team</p>
</div>
<div class="text-center">
<div class="w-20 h-20 rounded-full mx-auto mb-2 overflow-hidden">
<img src="images/organizers/jakob.webp" alt="Jakob Sauer Jørgensen" class="w-full h-full object-cover object-center">
</div>
<h4 class="font-bold text-sm mb-0.5">Jakob Sauer Jørgensen</h4>
<p class="text-gray-600 text-xs mb-0.5">Associate Professor, Technical University of Denmark</p>
<p class="text-gray-500 text-xs">Infrastructure Team</p>
</div>
<div class="text-center">
<div class="w-20 h-20 rounded-full mx-auto mb-2 overflow-hidden">
<img src="images/organizers/kjer.jpeg" alt="Hans Martin Kjer" class="w-full h-full object-cover object-top">
</div>
<h4 class="font-bold text-sm mb-0.5">Hans Martin Kjer</h4>
<p class="text-gray-600 text-xs mb-0.5">Associate Professor, Technical University of Denmark</p>
<p class="text-gray-500 text-xs">Infrastructure Team</p>
</div>
<div class="text-center">
<div class="w-20 h-20 rounded-full mx-auto mb-2 overflow-hidden">
<img src="images/organizers/Kirsten_person.jpeg" alt="Kirsten Korsholm" class="w-full h-full object-cover">
</div>
<h4 class="font-bold text-sm mb-0.5">Kirsten Korsholm</h4>
<p class="text-gray-600 text-xs mb-0.5">MD, Rigshospitalet</p>
<p class="text-gray-500 text-xs">Clinical Team</p>
</div>
<div class="text-center">
<div class="w-20 h-20 rounded-full mx-auto mb-2 overflow-hidden">
<img src="images/organizers/Ian_person.jpeg" alt="Ian Law" class="w-full h-full object-cover">
</div>
<h4 class="font-bold text-sm mb-0.5">Ian Law</h4>
<p class="text-gray-600 text-xs mb-0.5">Professor, Rigshospitalet</p>
<p class="text-gray-500 text-xs">Clinical Team</p>
</div>
</div>
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<h2 class="text-4xl md:text-5xl font-bold mb-2">Resources & Documentation</h2>
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Everything you need to participate in the challenge
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<h3 class="text-2xl font-bold">Codabench</h3>
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Official challenge platform:
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Visit Codabench
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<h3 class="text-2xl font-bold text-gray-900">GitHub</h3>
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Ressources for getting started:
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<span class="text-gray-700">Reconstruction containers</span>
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<span class="text-gray-700">Scoring metrics</span>
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<a href="https://github.com/bic-mac-challenge/challenge-codebase" target="_blank" rel="noopener noreferrer" class="inline-flex items-center bg-gray-900 text-white px-6 py-3 rounded-lg font-bold hover:bg-gray-800 transition-colors">
Visit GitHub
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<h2 class="text-4xl md:text-5xl font-bold mb-6">How to Participate</h2>
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Follow these three steps to get started.
</p>
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<!-- Step 1 -->
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1
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<div class="mt-6">
<h3 class="text-xl font-bold mb-3 text-blue-900">Sign Up on Codabench</h3>
<p class="text-gray-600 mb-6">
Create an account and register for the challenge on the Codabench platform.
</p>
<a href="https://www.codabench.org/competitions/12555/#/participate-tab" target="_blank" rel="noopener noreferrer" class="inline-flex items-center text-blue-600 font-semibold hover:text-blue-800 transition-colors">
Go to Codabench
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2
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<h3 class="text-xl font-bold mb-3 text-purple-900">Register Your Team</h3>
<p class="text-gray-600 mb-6">
Fill out the team registration form to officially join the challenge.
</p>
<a href="https://forms.gle/nNHhRw37EkSC2GE89" target="_blank" rel="noopener noreferrer" class="inline-flex items-center text-purple-600 font-semibold hover:text-purple-800 transition-colors">
Register Here
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3
</div>
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<div class="mt-6">
<h3 class="text-xl font-bold mb-3 text-indigo-900">Download Dataset</h3>
<p class="text-gray-600 mb-6">
Request access and download the challenge dataset from Hugging Face.
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