ArticleIEEE journal of translational engineering in health and medicine2023
TransU²-Net: An Effective Medical Image Segmentation Framework Based on Transformer and U²-Net.
Article in IEEE journal of translational engineering in health and medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- CT-Based Liver Segmentation for Liver Surgery: A Hybrid Approach Based on 3D U-Net-ELM Model.Biomedicines · 2026Article
- Liver Tumor Segmentation Based on Multi-Scale Deformable Feature Fusion and Global Context Awareness.Biomimetics (Basel, Switzerland) · 2025Article
- ICT-Net: An Integrated Convolution and Transformer-Based Network for Complex Liver and Liver Tumor Region Segmentation.IEEE journal of translational engineering in health and medicine · 2025Article
- A novel metastatic tumor segmentation method with a new evaluation metric in clinic study.Frontiers in medicine · 2024Article
- Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.Journal of hematology & oncology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the past few years, U-Net based U-shaped architecture and skip-connections have made incredible progress in the field of medical image segmentation. U PURPOSE: A 2D network structure TransU
methodsThe light-weight U
resultsOur proposed model TransU
conclusionsWe propose an automatic medical image segmentation method combining transformers and U
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.