Search Results for author:
Found 26 papers, 15 papers with code
Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios
However, existing methods overlook the non-independent and identically distributed (non-IDD) issue stemming from multidomain brain connectivity heterogeneity, in which data domains are drawn from different hospitals and imaging modalities.
MAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer
To the best of our knowledge, our MAST is the first transformer model dedicated to video polyp segmentation.
Edge-aware Feature Aggregation Network for Polyp Segmentation
Besides, a Scale-aware Convolution Module (SCM) is proposed to learn scale-aware features by using dilated convolutions with different ratios, in order to effectively deal with scale variation.
Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes
Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development.
Classification of lung cancer subtypes on CT images with synthetic pathological priors
The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements.
Pre-train, Adapt and Detect: Multi-Task Adapter Tuning for Camouflaged Object Detection
Camouflaged object detection (COD), aiming to segment camouflaged objects which exhibit similar patterns with the background, is a challenging task.
Predictive Experience Replay for Continual Visual Control and Forecasting
In this paper, we present a new continual learning approach for visual dynamics modeling and explore its efficacy in visual control and forecasting.
Camouflaged Object Detection via Context-aware Cross-level Fusion
To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects.
Continual Predictive Learning from Videos
Can we develop predictive learning algorithms that can deal with more realistic, non-stationary physical environments?
Video Polyp Segmentation: A Deep Learning Perspective
We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era.
Ranked #2 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
Specificity-preserving RGB-D Saliency Detection
To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and then propagate the fused feature to the next layer for integrating cross-level information.
Multi-Modal Transformer for Accelerated MR Imaging
To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging.
Context-aware Cross-level Fusion Network for Camouflaged Object Detection
Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings.
Progressively Normalized Self-Attention Network for Video Polyp Segmentation
Existing video polyp segmentation (VPS) models typically employ convolutional neural networks (CNNs) to extract features.
Ranked #6 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
Learning Synergistic Attention for Light Field Salient Object Detection
We propose a novel Synergistic Attention Network (SA-Net) to address the light field salient object detection by establishing a synergistic effect between multi-modal features with advanced attention mechanisms.
Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction
We evaluate the performance of the proposed model on the acceleration of multi-coil MR image reconstruction.
Towards Accurate RGB-D Saliency Detection with Complementary Attention and Adaptive Integration
In this paper, we propose Complementary Attention and Adaptive Integration Network (CAAI-Net), a novel RGB-D saliency detection model that integrates complementary attention based feature concentration and adaptive cross-modal feature fusion into a unified framework for accurate saliency detection.
Accurate Camouflaged Object Detection via Mixture Convolution and Interactive Fusion
Our method detects camouflaged objects with an effective fusion strategy, which aggregates the rich context information from a large receptive field.
Enhanced Information Fusion Network for Crowd Counting
We introduce the Information Fusion Module (IFM) which provides a channel for information flow to help different columns to obtain significant information from another column.
EF-Net: A novel enhancement and fusion network for RGB-D saliency detection
Finally, we propose an effective layer-wise aggregation module to fuse the features extracted from the enhanced depth maps and RGB images for the accurate detection of salient objects.
Uniqueness and weak-BV stability for $2\times 2$ conservation laws
As a consequence of our result, the Tame Oscillation Condition, and the Bounded Variation Condition on space-like curves are not necessary for the uniqueness of solutions in the $BV$ theory, in the case of systems with 2 unknowns.
Analysis of PDEs 35L65 (Primary) 76N15, 35L45, 35B35 (Secondary)
PraNet: Parallel Reverse Attention Network for Polyp Segmentation
To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images.
Ranked #7 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis.
Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE)
Diffusion MRI (dMRI) is a unique imaging technique for in vivo characterization of tissue microstructure and white matter pathways.
Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis
Medical image synthesis has been proposed as an effective solution to this, where any missing modalities are synthesized from the existing ones.
DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks
GCNNs are capable of extracting the geometric features of each fiber tract and harnessing the resulting features for accurate fiber parcellation and ultimately avoiding the use of atlases and any registration method.
