Federated Class-Heterogeneous Report Labeling with Surgical Aggregation
Keywords: Radiology, Report Labeling, Federated Learning, Surgical Aggregation, Class Heterogeneity
TL;DR: Federated Class-Heterogeneous Radiology Report Labeling with Surgical Aggregation
Abstract: Labeling radiology reports is essential for creating medical imaging datasets and enabling AI-driven clinical decision support. While SBERT-based classifiers offer computationally efficient solutions for this task, a major challenge is the class heterogeneity across datasets, as different groups focus on extracting distinct disease labels. For instance, NIH and CheXpert CXR datasets share only 7 of their 14 and 13 labels, respectively. To address this, we propose to use Surgical Aggregation, a class-heterogeneous federated learning framework that collaboratively trains a global multi-label classifier without requiring alignment of labeling schemes across clients. Surgical Aggregation selectively merges shared class weights while appending new disease-specific nodes, thereby unifying distinct local labeling priorities, to dynamically incorporate all disease labels of interest. We evaluated Surgical Aggregation in multiple simulated settings with varying number of participating nodes as well as different degrees of overlapping labels. Our results demonstrate high performance confirming adaptability in class-heterogeneous environments, thereby offering a scalable and privacy-preserving solution for collaborative medical report labeling. Our code is available at https://github.com/BioIntelligence-Lab/Federated-MedEmbedX
Primary Subject Area: Federated Learning
Secondary Subject Area: Learning with Noisy Labels and Limited Data
Paper Type: Validation or Application
Registration Requirement: Yes
Reproducibility: https://github.com/BioIntelligence-Lab/Federated-MedEmbedX
Visa & Travel: Yes
Submission Number: 256
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