mlsanityData to 14 Oct 2026

Multi-dimensional Fusion and Consistency for Semi-supervised Medical Image Segmentation

arXiv 2309.06618 ↗First posted 12 Sep 2023cs.CV q-bio.TOcited by 1

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Abstract (arXiv)

In this paper, we introduce a novel semi-supervised learning framework tailored for medical image segmentation. Central to our approach is the innovative Multi-scale Text-aware ViT-CNN Fusion scheme. This scheme adeptly combines the strengths of both ViTs and CNNs, capitalizing on the unique advantages of both architectures as well as the complementary information in vision-language modalities. Further enriching our framework, we propose the Multi-Axis Consistency framework for generating robust pseudo labels, thereby enhancing the semisupervised learning process. Our extensive experiments on several widelyused datasets unequivocally demonstrate the efficacy of our approach.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 38 references that are in the map. First posted on arXiv 12 Sep 2023.

Builds on

25 of 38 references.

References · 25

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  5. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  6. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  7. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  8. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  9. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,057
  10. 2019Publicly Available Clinical BERT Embeddingscited by 1,002
  11. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 959
  12. 2022Language-driven Semantic Segmentationcited by 717
13 more
  1. 2021DenseCLIP: Language-Guided Dense Prediction with Context-Aware Promptingcited by 532
  2. 2021Image Segmentation Using Text and Image Promptscited by 512
  3. 2021CRIS: CLIP-Driven Referring Image Segmentationcited by 353
  4. 2021XCiT: Cross-Covariance Image Transformerscited by 338
  5. 2020UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentationcited by 332
  6. 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 320
  7. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  8. 2021UTNet: A Hybrid Transformer Architecture for Medical Image Segmentationcited by 116
  9. 2022LViT: Language meets Vision Transformer in Medical Image Segmentationcited by 111
  10. 2021Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformercited by 100
  11. 2020SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentationcited by 71
  12. 2022OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Imagescited by 48
  13. 2021GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentationcited by 20

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024A Survey of Medical Vision-and-Language Applications and Their Techniquescited by 11