Jul 2026· Journal of imaging informatics in medicine· 0 citations· 9 references
Medicine
TL;DR
It is suggested that FS-DANet can substantially improve segmentation accuracy in complex ultrasound environments and holds strong potential for clinical application in personalized diagnosis.
An uncertainty-guided null-space diffusion (UGNS) framework is proposed, a novel label-free solution that enforces consistency correction on a stabilized positive-envelope proxy obtained via inverse log compression and achieves competitive generalized contrast-to-noise ratio values across diverse datasets.
Multi-phase contrast-enhanced CT (CECT) is widely employed to capture the dynamic enhancement patterns and temporal evolution of organs and lesions. However, acquiring multiple phases increases radiation exposure and is inevitably accompanied by inter-phase misalignment and inconsistencies due to patient motion and the...
Xing Li, Miao-Miao Wang, Bao-Ping Zhang et al.· IEEE Transactions on Image P...· 0 citations
This work proposes GSCA-UNet (Gated Spatial-Channel Attention UNet), a novel segmentation architecture for skin lesions that consistently outperforms competitive baselines across multiple metrics and exhibits superior robustness on challenging cases with blurry borders, irregular shapes, and severe artifacts.
Quantitative assessment of neuromuscular disorders relies on accurate musculoskeletal ultrasound (MSKUS) image segmentation. However, speckle noise and ambiguous anatomical boundaries pose continuous challenges. Existing convolutional networks often exhibit limitations in balancing detail preservation with noise suppre...
Yan Zhang, Ze-Tong Cai, Xiao-Qing Zhang et al.· International journal of ima...· 0 citations
Ultrasound imaging relies on high-frequency sound waves to visualize internal anatomy in a non-invasive manner, yet automated analysis remains difficult due to low signal-to-noise ratios, speckle artifacts, and inconsistencies across scanners. These challenges are compounded by the scarcity of annotated datasets, limit...
Satyam Dubey, Tanushree Meena, B. Bozorgtabar et al.· IEEE Access· 0 citations
Magnetic resonance imaging (MRI) plays an important role in clinical diagnosis, nevertheless, it suffers from motion artifacts in many real-world scenarios. Removing motion artifacts can alleviate this interference and thus facilitate precise diagnosis. However, recent works mainly rely on paired data and the artifactu...
Jia-Hua Xu, Dawei Zhou, Lei Hu et al.· Neural Networks· 0 citations
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