autoPET/CT IV Challenge at MICCAI 2025
48 views
Skip to first unread message
Pauline Ornela Megne Choudja
Apr 11, 2025, 10:12:12 AM4/11/25
to ch...@aims.ac.za, women-in-mac...@googlegroups.com
Dear All, FYI
🚨 The autoPET/CT IV Challenge has officially launched! 🚨
We’re excited to invite you to participate in the fourth edition of the autoPET Challenge, hosted at MICCAI2025 and supported by ESHI (European Society for hybrid, molecular and translational imaging).
🧠 This year’s challenge is all about exploring human-in-the-loop AI for lesion segmentation in two powerful imaging tasks:
1️⃣ Whole-body PET/CT (single-staging)
2️⃣ Longitudinal CT (follow-up screening)
📊 Why it matters
Current cancer imaging workflows rely heavily on manual interpretation, which is time-consuming and often limited to simple size-based measurements. Our goal: enable more robust, automated, and clinically useful AI tools that integrate human expertise and scale across modalities, timepoints, and imaging centers.
💡 What’s new this year?
We’re putting human annotations into the loop! Varying levels of expert input (e.g., clicks on lesions or background) are simulated and integrated into training and testing. You can build:
🔹 Novel deep learning approaches
🔹 Data-centric pipelines
🔹 Methods using (or fine-tuning) foundation models
🏆 Challenge Tracks & Prizes
Task 1 (PET/CT): €3,000 in total prizes
Task 2 (CT follow-ups): €6,000 in total prizes
📈 Whether you’re into model innovation or dataset-driven optimization, we seek solutions that adapt to multi-center, multi-tracer PET/CT data and longitudinal CT follow-ups!
👉 Learn more & join: https://lnkd.in/eVkDVBBk
We’re excited to invite you to participate in the fourth edition of the autoPET Challenge, hosted at MICCAI2025 and supported by ESHI (European Society for hybrid, molecular and translational imaging).
🧠 This year’s challenge is all about exploring human-in-the-loop AI for lesion segmentation in two powerful imaging tasks:
1️⃣ Whole-body PET/CT (single-staging)
2️⃣ Longitudinal CT (follow-up screening)
📊 Why it matters
Current cancer imaging workflows rely heavily on manual interpretation, which is time-consuming and often limited to simple size-based measurements. Our goal: enable more robust, automated, and clinically useful AI tools that integrate human expertise and scale across modalities, timepoints, and imaging centers.
💡 What’s new this year?
We’re putting human annotations into the loop! Varying levels of expert input (e.g., clicks on lesions or background) are simulated and integrated into training and testing. You can build:
🔹 Novel deep learning approaches
🔹 Data-centric pipelines
🔹 Methods using (or fine-tuning) foundation models
🏆 Challenge Tracks & Prizes
Task 1 (PET/CT): €3,000 in total prizes
Task 2 (CT follow-ups): €6,000 in total prizes
📈 Whether you’re into model innovation or dataset-driven optimization, we seek solutions that adapt to multi-center, multi-tracer PET/CT data and longitudinal CT follow-ups!
👉 Learn more & join: https://lnkd.in/eVkDVBBk
Pauline Ornela Megne Choudja
Personal Webpage: Ornela Megne
DISCLAIMER: The contents of this email and any attachments are confidential. They are intended for the named recipient(s) only. If you have received this email by mistake, please notify the sender immediately and you are herewith notified that the contents are legally privileged and that you do not have permission to disclose the contents to anyone, make copies thereof, retain or distribute or act upon it by any means, electronically, digitally or in print. The views expressed in this communication may be of a personal nature and not be representative of AIMS-NEI and/or any of its Centres or Initiatives.
Reply all
Reply to author
Forward
0 new messages