arxiv_cv 95% Match Research Paper Medical Imaging Researchers,Federated Learning Practitioners,AI in Healthcare Developers 17 hours ago

Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging

computer-vision › medical-imaging

📄 Abstract

Abstract: Federated learning is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often face challenges like partially labeled datasets, where only a few locations have certain expert annotations, leaving large portions of unlabeled data unused. Leveraging these could enhance transformer architectures ability in regimes with small and diversely annotated sets. We conduct the largest federated cardiac CT analysis to date (n=8,104) in a real-world setting across eight hospitals. Our two-step semi-supervised strategy distills knowledge from task-specific CNNs into a transformer. First, CNNs predict on unlabeled data per label type and then the transformer learns from these predictions with label-specific heads. This improves predictive accuracy and enables simultaneous learning of all partial labels across the federation, and outperforms UNet-based models in generalizability on downstream tasks. Code and model weights are made openly available for leveraging future cardiac CT analysis.

Key Contributions

This paper introduces a novel two-step semi-supervised strategy for federated learning in cardiac CT imaging, leveraging knowledge distillation from CNNs into a transformer architecture. This approach effectively utilizes partially labeled and unlabeled data across multiple hospitals, improving predictive accuracy and generalizability, which is crucial for real-world federated learning applications with data heterogeneity.

Business Value

Enables more effective and privacy-preserving analysis of medical imaging data across institutions, potentially leading to earlier disease detection and improved patient outcomes. It addresses the challenge of data silos in healthcare by allowing collaborative model training without sharing raw patient data.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

High, as it builds upon established federated learning and knowledge distillation techniques, and aims to work with real-world, partially labeled data.

Limitations Addressed

Partially labeled datasets in federated learning,Underutilization of unlabeled data,Limited generalizability of models across diverse datasets

Technical Tags

federated learningknowledge distillationtransformersemi-supervised learningcardiac CTCNNprivacy-preservingmedical imagingunlabeled datapartial labels

Research Topics

Federated LearningMedical Image AnalysisSemi-Supervised LearningModel CompressionPrivacy in AI

Methods & Architectures

Federated LearningKnowledge DistillationSemi-Supervised LearningTransformer NetworksCNNs TransformerCNNUNet

Applications & Tasks

Healthcare Medical Imaging Data ScarcityPrivacy ConcernsPartially Labeled DataModel Generalization Cardiac CT SegmentationMedical Image Classification

Related Fields

Machine LearningComputer VisionMedical InformaticsData Privacy

Keywords

Federated LearningKnowledge DistillationTransformerCardiac CTMedical ImagingSemi-Supervised LearningPrivacyDeep LearningUnlabeled DataPartial LabelsGeneralizationHealthcare AI

Academic Context

#Federated Learning#Medical Image Analysis#Semi-Supervised Learning#Model Compression#Privacy in AI

Commercial Potential

Potential Products

Federated learning platforms for medical imagingAI-powered diagnostic tools for cardiac CT

Target Industries

HealthcareMedical DevicesPharmaceuticals

Use Case Examples

Collaborative training of cardiac disease detection models across hospitalsImproving diagnostic accuracy for rare conditions using federated data

Competitive Edge

Offers a more robust and privacy-preserving alternative to traditional centralized training or simpler federated learning approaches, especially in scenarios with heterogeneous and incomplete data.

Market Opportunity

Growing market for AI in medical imaging and federated learning solutions.

Revenue Models

SaaS for AI diagnostic platformslicensing of models/algorithms

Resource Requirements

Compute Needs

Moderate to High (Federated learning typically requires distributed compute resources)

Data Requirements

Large, multi-institutional, partially labeled cardiac CT datasets

Deployment Constraints

Data privacy regulations (HIPAA, GDPR),Inter-institutional data sharing agreements,Computational resources at participating sites

Scalability

Scales with the number of participating hospitals and the size of the federated dataset.

Regulatory Considerations

Compliance with medical data privacy laws (e.g.HIPAAGDPR)

Production Readiness

Maturity Level

Research

Time to Market

1-3 years (for clinical adoption)

Licensing

Likely open-source (based on 'Code and model weights are made')

Patent Potential

Moderate (Novel algorithmic approaches in federated learning and knowledge distillation)

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