ACU-TransNet: Attention and convolution-augmented UNet-transformer network for polyp segmentation

Experimental results on the CVC-ClinicDB and Kvasir-SEG datasets demonstrate that mcU-TransNet outperforms existing state-of-the-art methods, showcasing its robustness.

Authors

Institutions

Publication Details

Journal
Journal of X-Ray Science and Technology
Published
2024-10-12
DOI
https://doi.org/10.3233/xst-240076
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.40
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ACU-TransNet: Attention and convolution-augmented UNet-transformer network for polyp segmentation

Lei Huang, Yun Wu
1 citations
Journal of X-Ray Science and Technology
Radiomics and Machine Learning in Medical Imaging
0.40
article

ACU-TransNet: Attention and convolution-augmented UNet-transformer network for polyp segmentation

Lei Huang, Yun Wu
article en
1 citations

Abstract

Experimental results on the CVC-ClinicDB and Kvasir-SEG datasets demonstrate that mcU-TransNet outperforms existing state-of-the-art methods, showcasing its robustness.

Journal of X-Ray Science and TechnologyVol. 32(6)
Guizhou University (CN)
Openalex Percentile: Top 34%
Radiomics and Machine Learning in Medical Imaging
0.40
DOISourceOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

ACU-TransNet: Attention and convolution-augmented UNet-transformer network for polyp segmentation — Lei Huang, Yun Wu · Journal of X-Ray Science and Technology (2024) | TGRS Research Map | TGRS