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Found 17 papers, 8 papers with code
Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout
To mitigate this issue and facilitate training of large models on edge devices, we introduce a simple yet effective strategy, Federated Layer-wise Learning, to simultaneously reduce per-client memory, computation, and communication costs.
MoViT: Memorizing Vision Transformers for Medical Image Analysis
We evaluate our method on a public histology image dataset and an in-house MRI dataset, demonstrating that MoViT applied to varied medical image analysis tasks, can outperform vanilla transformer models across varied data regimes, especially in cases where only a small amount of annotated data is available.
Deep-learning-enabled Brain Hemodynamic Mapping Using Resting-state fMRI
Deep-learning resting-state vascular imaging has the potential to become a useful tool in clinical cerebrovascular imaging.
Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination
In Heterogeneous Face Recognition (HFR), the objective is to match faces across two different domains such as visible and thermal.
Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation
In this work, we propose a novel training framework FedSM to avoid the client drift issue and successfully close the generalization gap compared with the centralized training for medical image segmentation tasks for the first time.
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation
Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing.
On-the-Fly Test-time Adaptation for Medical Image Segmentation
During test-time, the model takes in just the new test image and generates a domain code to adapt the features of source model according to the test data.
Towards performant and reliable undersampled MR reconstruction via diffusion model sampling
Magnetic Resonance (MR) image reconstruction from under-sampled acquisition promises faster scanning time.
ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer
Accelerating magnetic resonance image (MRI) reconstruction process is a challenging ill-posed inverse problem due to the excessive under-sampling operation in k-space.
Reference-based Magnetic Resonance Image Reconstruction Using Texture Transformer
Deep Learning (DL) based methods for magnetic resonance (MR) image reconstruction have been shown to produce superior performance in recent years.
Over-and-Under Complete Convolutional RNN for MRI Reconstruction
Reconstructing magnetic resonance (MR) images from undersampled data is a challenging problem due to various artifacts introduced by the under-sampling operation.
Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning
However, the generalizability of models trained with the FL setting can still be suboptimal due to domain shift, which results from the data collected at multiple institutions with different sensors, disease types, and acquisition protocols, etc.
Cuid: A new study of perceived image quality and its subjective assessment
It is thus critical to acquire reliable subjective data with controlled perception experiments that faithfully reflect human behavioural responses to distortions in visual signals.
Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images in Patients with Post-treatment Malignant Gliomas
Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas in neuro-oncology, especially with the help of standard anatomic and advanced molecular MR images.
Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-View Geometry
In this paper, we depart from the multi-person 3D pose estimation formulation, and instead reformulate it as crowd pose estimation.
Ranked #12 on
3D Multi-Person Pose Estimation
on Panoptic
(using extra training data)
3D Multi-Person Human Pose Estimation
3D Multi-Person Pose Estimation
+2
Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN
Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas for patients with malignant gliomas in neuro-oncology with the help of conventional and advanced molecular MR images.
Customized OCT images compression scheme with deep neural network
We added customized skip connections between the compression CNNs and the reconstruction CNNs to reserve the detail information and trained the two nets together with the semantic segmented image patches from data preprocessing module.
