Search Results for author:
Found 9 papers, 4 papers with code
UDCR: Unsupervised Aortic DSA/CTA Rigid Registration Using Deep Reinforcement Learning and Overlap Degree Calculation
In this paper, we propose an unsupervised method, UDCR, for aortic DSA/CTA rigid registration based on deep reinforcement learning.
An Automatic Cascaded Model for Hemorrhagic Stroke Segmentation and Hemorrhagic Volume Estimation
Hemorrhagic Stroke (HS) has a rapid onset and is a serious condition that poses a great health threat.
Cross-head mutual Mean-Teaching for semi-supervised medical image segmentation
To address these concerns, we propose a novel Cross-head mutual mean-teaching Network (CMMT-Net) incorporated strong-weak data augmentation, thereby benefitting both self-training and consistency learning.
Two-Stage Hybrid Supervision Framework for Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT
Abdominal organ and tumour segmentation has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis.
TSI-Net: A Timing Sequence Image Segmentation Network for Intracranial Artery Segmentation in Digital Subtraction Angiography
Therefore, 2D DSA segmentation methods are unable to capture the complete IA information and treatment of cerebrovascular diseases.
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences
In this work, we introduce DIAS, a dataset specifically developed for IA segmentation in DSA sequences.
Combining Self-Training and Hybrid Architecture for Semi-supervised Abdominal Organ Segmentation
Abdominal organ segmentation has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis.
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma
According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS.
Data Augmentation
Weakly supervised Semantic Segmentation
+1
PHTrans: Parallelly Aggregating Global and Local Representations for Medical Image Segmentation
In this paper, we propose a novel hybrid architecture for medical image segmentation called PHTrans, which parallelly hybridizes Transformer and CNN in main building blocks to produce hierarchical representations from global and local features and adaptively aggregate them, aiming to fully exploit their strengths to obtain better segmentation performance.
