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Yinghong Yu
ELLIS Institute Finland, Aalto University
G
Guangyuan Li
Aalto University
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References (34)
[3]
MedDINOv3: How to adapt vision foundation models for medical image segmentation?
Founder's Pitch
"Develop 3D-enabled AI models from existing 2D models without retraining, leveraging PlaneCycle's adapter-free technology."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
Quick Build
4/4 signals
Series A Potential
4/4 signals
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Why It Matters
This research allows leveraging vast existing investments in 2D foundation models by enabling them to handle 3D data without retraining, thus saving resources and time especially critical in fields like medical imaging.
Product Angle
Develop an API that enables existing AI models to extend their capabilities to 3D datasets, primarily targeting industries with volumetric data requirements such as healthcare and scientific research.
Disruption
It replaces the need for specialized 3D models or adapting 2D models with additional computational layers, offering a streamlined process for enabling 3D capability.
Product Opportunity
As medical imaging and similar fields increasingly rely on 3D data processing, the ability to retrofit existing 2D models creates a significant market, potentially a multi-billion dollar industry as it affects major imaging device manufacturers and healthcare systems.
Use Case Idea
Adapt medical imaging models to efficiently process CT and MRI scans without needing to develop entirely new 3D models, saving time and computational resources.
Science
PlaneCycle cyclically distributes spatial aggregation across different planes using the original 2D backbone, allowing 3D fusion without architectural modifications or retraining, preserving pretrained 2D model biases.
Method & Eval
The method was evaluated on six 3D classification and three 3D segmentation tasks, showing superior performance over traditional methods both in zero-training and fine-tuning conditions, achieving close to fully trained 3D models.
Caveats
Performance could vary with model architecture; although promising, results depend on the compatibility of existing 2D models with PlaneCycle's method.
Author Intelligence
Yinghong Yu
ELLIS Institute Finland, Aalto University
Guangyuan Li
Aalto University
Jiancheng Yang
ELLIS Institute Finland, Aalto University
jiancheng.yang@aalto.fi