Shukai Yang

Orcid: 0009-0007-4305-6601

According to our database1, Shukai Yang authored at least 12 papers between 2023 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
DNUNet: A lightweight adaptive medical image segmentation network based on dual-path multilevel interactive convolution and norm sparse fusion module.
Neural Networks, 2026

2025
AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems.
CoRR, March, 2025

TBE-Net: A Deep Network Based on Tree-Like Branch Encoder for Medical Image Segmentation.
IEEE J. Biomed. Health Informatics, January, 2025

Enhanced medical image segmentation via deep dynamic self-adjusting U-Net with multi-scale attention and semantic mitigation.
Vis. Comput., 2025

Characterizing Coke Particle Size Distribution Using CondInst Instance Segmentation and LightGBM Regression Model.
IEEE Trans. Instrum. Meas., 2025

2024
An effective underground image enhancement approach based on improved KinD network.
Signal Image Video Process., September, 2024

An Efficient Segmentation Model With Multipath Attention Mechanism Enabling Particle Size Characterization of Coal Dust.
IEEE Trans. Ind. Informatics, April, 2024

FAFS-UNet: Redesigning skip connections in UNet with feature aggregation and feature selection.
Comput. Biol. Medicine, March, 2024

FCSU-Net: A novel full-scale Cross-dimension Self-attention U-Net with collaborative fusion of multi-scale feature for medical image segmentation.
Comput. Biol. Medicine, 2024

2023
UcUNet: A lightweight and precise medical image segmentation network based on efficient large kernel U-shaped convolutional module design.
Knowl. Based Syst., October, 2023

Ms-AMPool: Down-Sampling Method for Dense Prediction Tasks.
Proceedings of the Artificial Neural Networks and Machine Learning, 2023

Pie-UNet: A Novel Parallel Interaction Encoder for Medical Image Segmentation.
Proceedings of the Artificial Neural Networks and Machine Learning, 2023


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