ReviewSensors (Basel, Switzerland)2024
Next-Gen Medical Imaging: U-Net Evolution and the Rise of Transformers.
Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Deep Learning-Based OCT Segmentation for Stiffness Quantification in Evaluating Low-Level Laser Therapy for Wound Healing.Journal of biophotonics · 2026Trial
- Deep Learning in Dental Imaging: Advances, Challenges, and Future.Oral radiology · 2026Review
- Generating synthetic tau-PET scans in Alzheimer's disease from MRI, blood biomarkers and demographics with deep learning.medRxiv : the preprint server for health sciences · 2026Article
- Improved deep joint model with tuned improved LeNet for spinal cord segmentation and injury detection using CT images.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Redefining lightweight vision models for healthcare AI.Frontiers in artificial intelligence · 2026Article
- Current State of Evidence for Use of MRI in LI-RADS.Journal of magnetic resonance imaging : JMRI · 2025Review
- [Segmentation of anterior cruciate ligament images by fusing inflated convolution and residual hybrid attention].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Building extraction from remote sensing imagery using SegFormer with post-processing optimization.PloS one · 2025Article
- Applications, image analysis, and interpretation of computer vision in medical imaging.Frontiers in radiology · 2025Review
- Deep learning segmentation model for quantification of infarct size in pigs with myocardial ischemia/reperfusion.Basic research in cardiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advancement of medical imaging has profoundly impacted our understanding of the human body and various diseases. It has led to the continuous refinement of related technologies over many years. Despite these advancements, several challenges persist in the development of medical imaging, including data shortages characterized by low contrast, high noise levels, and limited image resolution. The U-Net architecture has significantly evolved to address these challenges, becoming a staple in medical imaging due to its effective performance and numerous updated versions. However, the emergence of Transformer-based models marks a new era in deep learning for medical imaging. These models and their variants promise substantial progress, necessitating a comparative analysis to comprehend recent advancements. This review begins by exploring the fundamental U-Net architecture and its variants, then examines the limitations encountered during its evolution. It then introduces the Transformer-based self-attention mechanism and investigates how modern models incorporate positional information. The review emphasizes the revolutionary potential of Transformer-based techniques, discusses their limitations, and outlines potential avenues for future research.
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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.