ArticleSensors (Basel, Switzerland)2026
RPFeaNet: Rethinking Deep Progressive Prompt-Guided Feature Interaction Fusion Network for Medical Ultrasound Image Segmentation.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Although ultrasound image segmentation has advanced significantly with deep learning, existing methods still suffer from a lack of prior knowledge guidance, partly due to the low-contrast, speckle-noise-corrupted nature from clinical ultrasound sensors. This paper proposes a novel ultrasound segmentation framework (RPFeaNet) that extracts progressive prompts from a low-to-high level prompt generation mechanism. Furthermore, the high-level prompt-guided feature interaction module (HPGFIM) fuses progressive prompt via Mamba blocks and stage-wise condition injection. The dynamic selective-frequency decoder (DSFD) combines dynamically selecting a strategy with the fusion of high-frequency details to suppress noise and refine edge details. Extensive experiments on six datasets demonstrate that RPFeaNet achieves state-of-the-art performance compared to existing methods, validating its strong generalization and robustness across diverse clinical ultrasound scenarios.
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.