Search Results for author:
Found 7 papers, 2 papers with code
KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level
Chronic kidney disease (CKD) is a major global health issue, affecting over 10% of the population and causing significant mortality.
FullTransNet: Full Transformer with Local-Global Attention for Video Summarization
Based on this, we propose a transformer-like architecture, named FullTransNet, which has a full encoder-decoder structure with local-global sparse attention for video summarization.
Pubic Symphysis-Fetal Head Segmentation Network Using BiFormer Attention Mechanism and Multipath Dilated Convolution
To address this issue, we introduce a dynamic, query-aware sparse attention mechanism for ultrasound image segmentation.
Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation using Rein to Fine-tune Vision Foundation Models
In recent years, significant progress has been made in tumor segmentation within the field of digital pathology.
PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5, 101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions.
BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation
Specifically, BRAU-Net++ uses bi-level routing attention as the core building block to design our u-shaped encoder-decoder structure, in which both encoder and decoder are hierarchically constructed, so as to learn global semantic information while reducing computational complexity.
Pubic Symphysis-Fetal Head Segmentation Using Pure Transformer with Bi-level Routing Attention
In this paper, we propose a method, named BRAU-Net, to solve the pubic symphysis-fetal head segmentation task.
