Search Results for author:
Found 7 papers, 2 papers with code
SemiCVT: Semi-Supervised Convolutional Vision Transformer for Semantic Segmentation
Semi-supervised learning improves data efficiency of deep models by leveraging unlabeled samples to alleviate the reliance on a large set of labeled samples.
ScaleFormer: Revisiting the Transformer-based Backbones from a Scale-wise Perspective for Medical Image Segmentation
(2) A simple and effective spatial-aware inter-scale transformer is designed to interact among consensual regions in multiple scales, which can highlight the cross-scale dependency and resolve the complex scale variations.
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting
In this work, we propose a novel LiTS method to adequately aggregate multi-phase information and refine uncertain region segmentation.
Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation
Our Graph- PGCR module is plug-and-play, which can be integrated into any architecture to improve its performance.
Graph-BAS3Net: Boundary-Aware Semi-Supervised Segmentation Network With Bilateral Graph Convolution
Semi-supervised learning (SSL) algorithms have attracted much attentions in medical image segmentation by leveraging unlabeled data, which challenge in acquiring massive pixel-wise annotated samples.
UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
UNet, which is one of deep learning networks with an encoder-decoder architecture, is widely used in medical image segmentation.
Ranked #1 on
Medical Image Segmentation
on LiTS2017
Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations
This is the first time to study privacy protection on targeted links against link-prediction-based attacks.
Social and Information Networks Physics and Society
