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Found 19 papers, 18 papers with code
FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis
In this paper, we attribute this competition to the homogeneity in loss patterns exhibited by rare and mislabeled data clients, preventing existing loss-based fair and robust FL methods from effectively distinguishing and handling these two distinct client types.
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching
Existing fair federated learning methods have demonstrated some effectiveness in solving this problem by aligning a single 0th- or 1st-order state of convergence (e. g., training loss or sharpness).
PASSION: Towards Effective Incomplete Multi-Modal Medical Image Segmentation with Imbalanced Missing Rates
In this paper, we, for the first time, formulate such a challenging setting and propose Preference-Aware Self-diStillatION (PASSION) for incomplete multi-modal medical image segmentation under imbalanced missing rates.
Non-parametric regularization for class imbalance federated medical image classification
Limited training data and severe class imbalance pose significant challenges to developing clinically robust deep learning models.
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness
We begin by evaluating the completeness of annotations at the client level using a designed indicator.
FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity
Experiments on two publicly-available medical datasets validate the superiority of FedMLP against the state-of-the-art both federated semi-supervised and noisy label learning approaches under task heterogeneity.
From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching
In this study, we pioneer the identification and formulation of this new fairness challenge within the context of the imaging quality shift.
SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts
Specifically, based on SAM, SAMCT is further equipped with a U-shaped CNN image encoder, a cross-branch interaction module, and a task-indicator prompt encoder.
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise
Specifically, noise estimation at each client is accomplished through the Gaussian mixture model and then incorporated into model aggregation in a layer-wise manner to up-weight high-quality clients.
Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting
End-to-end medical image segmentation is of great value for computer-aided diagnosis dominated by task-specific models, usually suffering from poor generalization.
ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation
Following CSA, 2D convolution is utilized for feature refinement through CFFN.
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity
Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning.
FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring
To overcome this, we propose federated classifier anchoring (FCA) by adding a personalized classifier at each client to guide and debias the federated model through consistency learning.
Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation
Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms.
The Lighter The Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning
To our best knowledge, this is the first work on transformer pruning for medical image analysis tasks.
BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation
Specifically, to fully explore the benefits of transformers in long-range dependency establishment, a cross-scale global transformer (CGT) module is introduced to jointly utilize multiple small-scale feature maps for richer global features with lower computational complexity.
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification
In this paper, we present a privacy-preserving FL method named FedIIC to combat class imbalance from two perspectives: feature learning and classifier learning.
FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation
To relax this assumption, in this work, we propose a label-agnostic unified federated learning framework, named FedMix, for medical image segmentation based on mixed image labels.
Exploring intermediate representation for monocular vehicle pose estimation
The latter question motivates us to incorporate geometry knowledge with a new loss function based on a projective invariant.
Ranked #1 on
Vehicle Pose Estimation
on KITTI Cars Hard
