Article

Flexible and Scalable Federated Learning with Deep Feature Prompts for Digital Pathology

2025-08-08

Abstract excerpt

<title>Abstract</title> <p> Collaborative learning across medical institutions is considered essential for developing robust and generalisable models in digital pathology. Federated learning (FL) offers a promising framework for enabling collaboration without the need to centralise data. Still, its practical adoption remains limited due to challenges such as high communication overhead, model heterogeneity acros...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: dd3a808f-3e99-5112-81dc-c510e0fbea87
DOI: 10.21203/rs.3.rs-7060589/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Flexible and Scalable Federated Learning with Deep Feature Prompts for Digital PathologyDOI 10.21203/rs.3.rs-7060589/v1Read

Federated CT Foundation Models for Multi-Center Detection of Lymph Node Metastasis in Pancreatic CancerSemantic proximity Read

Federated Learning-Driven Collaborative Diagnostic System for Metastatic Breast CancerSemantic proximity Read

FTAD-Net: Federated Transfer-Adversarial Learning for Robust Breast Cancer DetectionSemantic proximity Read

Individualized Per-Site Meta-Federated Feature Learning (iPS-MFFL) for Privacy-Preserving Brain Tumor MRI Classification under non-IID HeterogeneitySemantic proximity Read

Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular DiseaseSemantic proximity Read

Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic reviewSemantic proximity Read

Federated deep learning enables cancer subtyping by proteomicsSemantic proximity Read

Towards Federated Learning Across Biobanks: Prototype Software from the 2026 Carnegie Mellon University–NVIDIA HackathonSemantic proximity Read

Cross-institutional HER2 assessment via a computer-aided system using federated learning and stain composition augmentationSemantic proximity Read

FedIHRAS: A Privacy-Preserving Federated Learning Framework for Multi-Institutional Collaborative Radiological Analysis with Integrated Explainability and Automated Clinical ReportingSemantic proximity Read

Configuring a federated network of real-world patient health data for multimodal deep learning prediction of health outcomesSemantic proximity Read

Ten Quick Tips for Biomedical Federated LearningSemantic proximity Read