Search Results for author:
Found 8 papers, 2 papers with code
Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer
Lymph node (LN) assessment is a critical, indispensable yet very challenging task in the routine clinical workflow of radiology and oncology.
ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction and Synthetic Features
Detecting anomalies in fundus images through unsupervised methods is a challenging task due to the similarity between normal and abnormal tissues, as well as their indistinct boundaries.
Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning
To tackle the source data-absent problem, we present a novel two-stage source-free domain adaptation (SFDA) framework for medical image segmentation, where only a well-trained source segmentation model and unlabeled target data are available during domain adaptation.
Region and Spatial Aware Anomaly Detection for Fundus Images
ReSAD transfers a pre-trained model to extract the features of normal fundus images and applies the Region-and-Spatial-Aware feature Combination module (ReSC) for pixel-level features to build a memory bank.
Coarse Retinal Lesion Annotations Refinement via Prototypical Learning
Deep-learning-based approaches for retinal lesion segmentation often require an abundant amount of precise pixel-wise annotated data.
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.
Bilateral-ViT for Robust Fovea Localization
The fovea is an important anatomical landmark of the retina.
A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation
Despite the tremendous success of deep neural networks in medical image segmentation, they typically require a large amount of costly, expert-level annotated data.
