Search Results for author:
Found 10 papers, 7 papers with code
Fair Federated Medical Image Segmentation via Client Contribution Estimation
Recent studies have investigated how to reward clients based on their contribution (collaboration fairness), and how to achieve uniformity of performance across clients (performance fairness).
Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer
Our style representation is exceptionally lightweight and can hardly be used for the reconstruction of the dataset.
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance
Despite recent progress on semi-supervised federated learning (FL) for medical image diagnosis, the problem of imbalanced class distributions among unlabeled clients is still unsolved for real-world use.
DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images
Based on this estimated discrepancy, a dynamic learning rate adjustment strategy is then developed to achieve a suitable degree of adaptation for each test sample.
Histopathological Image Classification
Image Classification
+1
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation
Federated learning (FL) allows multiple medical institutions to collaboratively learn a global model without centralizing client data.
HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images
Multiple medical institutions collaboratively training a model using federated learning (FL) has become a promising solution for maximizing the potential of data-driven models, yet the non-independent and identically distributed (non-iid) data in medical images is still an outstanding challenge in real-world practice.
UniFed: A Unified Framework for Federated Learning on Non-IID Image Features
In this work, we propose a unified framework to tackle the non-iid issues for internal and external clients together.
Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems.
Ranked #1 on
Surgical phase recognition
on HeiChole Benchmark
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy.
