mlsanityData to 14 Oct 2026

Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment

arXiv 2303.00279 ↗First posted 1 Mar 2023eess.IV cs.CL cs.CVcited by 7

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Abstract (arXiv)

Segmentation of COVID-19 lesions can assist physicians in better diagnosis and treatment of COVID-19. However, there are few relevant studies due to the lack of detailed information and high-quality annotation in the COVID-19 dataset. To solve the above problem, we propose C2FVL, a Coarse-to-Fine segmentation framework via Vision-Language alignment to merge text information containing the number of lesions and specific locations of image information. The introduction of text information allows the network to achieve better prediction results on challenging datasets. We conduct extensive experiments on two COVID-19 datasets including chest X-ray and CT, and the results demonstrate that our proposed method outperforms other state-of-the-art segmentation methods.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 19 of its 26 references that are in the map. First posted on arXiv 1 Mar 2023.

Builds on

19 of 26 references.

References · 19

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
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  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  4. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,222
  5. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  6. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  7. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  8. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 586
  9. 2021UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformercited by 197
  10. 2022LViT: Language meets Vision Transformer in Medical Image Segmentationcited by 111
  11. 2020MosMedData: Chest CT Scans With COVID-19 Related Findings Datasetcited by 104
  12. 2018HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentationcited by 66
7 more
  1. 2022TGANet: Text-guided attention for improved polyp segmentationcited by 59
  2. 2020Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentationcited by 19
  3. 2022TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentationcited by 18
  4. 2020COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approachescited by 14
  5. 2020Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Imagescited by 13
  6. 2022Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistencycited by 6
  7. 2020COVID TV-UNet: Segmenting COVID-19 Chest CT Images Using Connectivity Imposed U-Netcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 1; 2026: 2.

7 papers.

  1. 2024ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentationcited by 24
  2. 2023ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embeddingcited by 23
  3. 2024Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging casescited by 19
  4. 2025STPNet: Scale-aware Text Prompt Network for Medical Image Segmentationcited by 6
  5. 2024Optimizing Universal Lesion Segmentation: State Space Model-Guided Hierarchical Networks with Feature Importance Adjustmentcited by 1
  6. 2026Scale-aware Adaptive Supervised Network with Limited Medical Annotationscited by 1
  7. 2026Evidential learning driven Breast Tumor Segmentation with Stage-divided Vision-Language Interactioncited by 1