Search Results for author:
Found 19 papers, 9 papers with code
Regression of Instance Boundary by Aggregated CNN and GCN
In particular, thanks to the proposed aggregation GCN, our network benefits from direct feature learning of the instances’ boundary locations and the spatial information propagation across the image.
Advancing Cross-Organ Domain Generalization with Test-Time Style Transfer and Diversity Enhancement
Deep learning has made significant progress in addressing challenges in various fields including computational pathology (CPath).
Incomplete Modality Disentangled Representation for Ophthalmic Disease Grading and Diagnosis
To address these, we propose an Incomplete Modality Disentangled Representation (IMDR) strategy, which disentangles features into explicit independent modal-common and modal-specific features by guidance of mutual information, distilling informative knowledge and enabling it to reconstruct valuable missing semantics and produce robust multimodal representations.
CathAction: A Benchmark for Endovascular Intervention Understanding
Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions.
CLIP-DR: Textual Knowledge-Guided Diabetic Retinopathy Grading with Ranking-aware Prompting
Diabetic retinopathy (DR) is a complication of diabetes and usually takes decades to reach sight-threatening levels.
SenseAI: Real-Time Inpainting for Electron Microscopy
Despite their proven success and broad applicability to Electron Microscopy (EM) data, joint dictionary-learning and sparse-coding based inpainting algorithms have so far remained impractical for real-time usage with an Electron Microscope.
Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images
Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature.
Automatically Segment the Left Atrium and Scars from LGE-MRIs Using a Boundary-focused nnU-Net
Accurate segmentation of the left atrial (LA) and LA scars can provide valuable information to predict treatment outcomes in AF.
Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model
Image segmentation is a fundamental task in the field of imaging and vision.
DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification
Multiple instance learning (MIL) has been increasingly used in the classification of histopathology whole slide images (WSIs).
Ranked #1 on
Multiple Instance Learning
on TCGA
3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs
In this paper, we propose a novel multi-level aggregation network to regress the coordinates of the vertices of a 3D face from a single 2D image in an end-to-end manner.
Counting with Adaptive Auxiliary Learning
This paper proposes an adaptive auxiliary task learning based approach for object counting problems.
BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation
Our model is well-suited to obtain global semantic region information while also accommodates local spatial boundary characteristics simultaneously.
Spatial Uncertainty-Aware Semi-Supervised Crowd Counting
Semi-supervised approaches for crowd counting attract attention, as the fully supervised paradigm is expensive and laborious due to its request for a large number of images of dense crowd scenarios and their annotations.
3D Vessel Reconstruction in OCT-Angiography via Depth Map Estimation
Experimental results demonstrate that our method is effective in the depth prediction and 3D vessel reconstruction for OCTA images.% results may be used to guide subsequent vascular analysis
Learning Euler's Elastica Model for Medical Image Segmentation
Inspired by Euler's Elastica model and recent active contour models introduced into the field of deep learning, we propose a novel active contour with elastica (ACE) loss function incorporating Elastica (curvature and length) and region information as geometrically-natural constraints for the image segmentation tasks.
CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging
Automated detection of curvilinear structures, e. g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases.
Development and Validation of a Novel Prognostic Model for Predicting AMD Progression Using Longitudinal Fundus Images
We propose a novel deep learning method to predict the progression of diseases using longitudinal imaging data with uneven time intervals, which requires no prior feature extraction.
ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model
To address these issues, for the first time in the field of retinal image analysis we construct a dedicated Retinal OCT-A SEgmentation dataset (ROSE), which consists of 229 OCT-A images with vessel annotations at either centerline-level or pixel level.
Ranked #1 on
Retinal Vessel Segmentation
on ROSE-1 DVC
