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Found 22 papers, 10 papers with code
Tackling Data Heterogeneity in Federated Learning via Loss Decomposition
To mitigate the impact of data heterogeneity on FL performance, we start with analyzing how FL training influence FL performance by decomposing the global loss into three terms: local loss, distribution shift loss and aggregation loss.
Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding
This paper introduces H-SAM: a prompt-free adaptation of SAM tailored for efficient fine-tuning of medical images via a two-stage hierarchical decoding procedure.
Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection
In this work, we propose to explore Self- and Cross-Triplet Correlations (SCTC) for HOI detection.
Human-Object Interaction Detection
Knowledge Distillation
+2
FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning
Federated learning (FL) is a powerful technology that enables collaborative training of machine learning models without sharing private data among clients.
FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning
Federated learning (FL) is an emerging paradigm in machine learning, where a shared model is collaboratively learned using data from multiple devices to mitigate the risk of data leakage.
A Decoupled Multi-Task Network for Shadow Removal
Last, these features are converted to a target shadow-free image, affiliated shadow matte, and shadow image, supervised by multi-task joint loss functions.
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography.
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging
The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing.
Masked Co-attentional Transformer reconstructs 100x ultra-fast/low-dose whole-body PET from longitudinal images and anatomically guided MRI
In this study, we utilize the global similarity between baseline and follow-up PET and magnetic resonance (MR) images to develop Masked-LMCTrans, a longitudinal multi-modality co-attentional CNN-Transformer that provides interaction and joint reasoning between serial PET/MRs of the same patient.
Learning MRI Artifact Removal With Unpaired Data
Retrospective artifact correction (RAC) improves image quality post acquisition and enhances image usability.
An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging
In this paper, we investigate the deleterious impact of a taxonomy of data heterogeneity regimes on federated learning methods, including quantity skew, label distribution skew, and imaging acquisition skew.
SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging
In this study, we propose a novel heterogeneity-aware federated learning method, SplitAVG, to overcome the performance drops from data heterogeneity in federated learning.
Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging
Specifically, instead of directly training a model for task performance, we develop a novel dual model architecture: a primary model learns the desired task, and an auxiliary "generative replay model" allows aggregating knowledge from the heterogenous clients.
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning
Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution.
Addressing catastrophic forgetting for medical domain expansion
Notably, this approach degrades model performance at the original institution, a phenomenon known as catastrophic forgetting.
The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions
Model brittleness is a primary concern when deploying deep learning models in medical settings owing to inter-institution variations, like patient demographics and intra-institution variation, such as multiple scanner types.
Federated Learning for Breast Density Classification: A Real-World Implementation
Building robust deep learning-based models requires large quantities of diverse training data.
DeshadowNet: A Multi-Context Embedding Deep Network for Shadow Removal
Two levels of features are derived from the global network and transferred to two parallel networks.
3D Hand Pose Tracking and Estimation Using Stereo Matching
This paper demonstrates that the performance of the state-of-the art tracking/estimation algorithms can be maintained with most stereo matching algorithms on the proposed benchmark, as long as the hand segmentation is correct.
RGBD Salient Object Detection via Deep Fusion
Numerous efforts have been made to design different low level saliency cues for the RGBD saliency detection, such as color or depth contrast features, background and color compactness priors.
Ranked #26 on
RGB-D Salient Object Detection
on NJU2K
Pixel-wise Orthogonal Decomposition for Color Illumination Invariant and Shadow-free Image
In this paper, we propose a novel, effective and fast method to obtain a color illumination invariant and shadow-free image from a single outdoor image.
