Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
October 15, 2025Open Access

Text-driven Multiplanar Visual Interaction for Semi-supervised Medical Image Segmentation

View Full Paper
Ask AI
Bookmark
Share

Authors

KHKaiwen HuangYZYi ZhouHFHuazhu Fu

Discussion

Loading...

Member takes

Overview

This approach introduces a text-driven framework for improving semi-supervised medical image segmentation, indicating potential for enhanced visual understanding.

Key Points

  • The proposed model significantly improves segmentation performance by integrating textual information with visual data.
  • Experiments on three public datasets show enhanced accuracy and robustness over existing methods.
  • The framework consists of modules that facilitate interaction between text and visual features, enhancing feature category awareness.
  • Dynamic Cognitive Augmentation effectively reduces discrepancies between labeled and unlabeled data, improving model training.

Cite This Study

Huang et al. (2025) studied this question.

synapsesocial.com/papers/68ef858cc6a308ba0635579ahttps://doi.org/10.48550/arxiv.2507.12382
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues2024
  2. 2Harnessing Text Insights with Visual Alignment for Medical Image Segmentation2025 · 23 citations
  3. 3Learning Conceptual Text Prompts from Visual Regions of Interest for Medical Image Segmentation2026
  4. 4Voxel-Level Text-Visual Alignment with Discrepancy-Aware Fusion for Semi-Supervised Multi-Organ Segmentation2026
  5. 5Decoupling Target Semantics via Text-anchored Visual Contrast for Semi-supervised Medical Image Segmentation2026 · 6 citations