mlsanityData to 7 Oct 2026

SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues

arXiv 2406.19364 ↗First posted 27 Jun 2024cs.CVcited by 6

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Abstract (arXiv)

Weakly-supervised medical image segmentation is a challenging task that aims to reduce the annotation cost while keep the segmentation performance. In this paper, we present a novel framework, SimTxtSeg, that leverages simple text cues to generate high-quality pseudo-labels and study the cross-modal fusion in training segmentation models, simultaneously. Our contribution consists of two key components: an effective Textual-to-Visual Cue Converter that produces visual prompts from text prompts on medical images, and a text-guided segmentation model with Text-Vision Hybrid Attention that fuses text and image features. We evaluate our framework on two medical image segmentation tasks: colonic polyp segmentation and MRI brain tumor segmentation, and achieve consistent state-of-the-art performance. Source code is available at: https://github.com/xyx1024/SimTxtSeg.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 27 Jun 2024.

Builds on

23 of 33 references.

References · 23

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  3. 2023Segment Anythingcited by 8,158
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  5. 2023ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoderscited by 703
  6. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 588
  7. 2020PraNet: Parallel Reverse Attention Network for Polyp Segmentationcited by 460
  8. 2019ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed datacited by 260
  9. 2020Medical Image Segmentation Using Deep Learning: A Surveycited by 163
  10. 2019Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmcited by 76
  11. 2021A multi-centre polyp detection and segmentation dataset for generalisability assessmentcited by 63
  12. 2023SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million maskscited by 54
11 more
  1. 2020Going to Extremes: Weakly Supervised Medical Image Segmentationcited by 38
  2. 2023Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray imagescited by 33
  3. 2023ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embeddingcited by 23
  4. 2023WeakPolyp: You Only Look Bounding Box for Polyp Segmentationcited by 21
  5. 2023S2ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentationcited by 15
  6. 2022BoxPolyp:Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotationscited by 10
  7. 2023Conditional Diffusion Models for Weakly Supervised Medical Image Segmentationcited by 8
  8. 2021Learning to Segment Medical Images from Few-Shot Sparse Labelscited by 6
  9. 2021Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervisioncited by 4
  10. 2022BoxShrink: From Bounding Boxes to Segmentation Maskscited by 4
  11. 2023Boosting Weakly-Supervised Image Segmentation via Representation, Transform, and Compensatorcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025Taming Vision-Language Models for Medical Image Analysis: A Comprehensive Reviewcited by 9
  2. 2025Text-guided multi-stage cross-perception network for medical image segmentationcited by 1
  3. 2025Prompt Mechanisms in Medical Imaging: A Comprehensive Surveycited by 0
  4. 2026Rad-VLSM: A Cross-Modal Framework with Semantics-Assisted Prompting for Medical Segmentation and Diagnosiscited by 0
  5. 2026Beyond Visual Cues: CoT-Enhanced Reasoning for Semi-supervised Medical Image Segmentationcited by 0
  6. 2026Understanding From Human Perspective: A Multi-agent System for Interactive Egocentric Medical Image Segmentationcited by 0