mlsanityData to 7 Oct 2026

CTS: A Consistency-Based Medical Image Segmentation Model

arXiv 2405.09056 ↗First posted 15 May 2024cs.CV cs.AIcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

In medical image segmentation tasks, diffusion models have shown significant potential. However, mainstream diffusion models suffer from drawbacks such as multiple sampling times and slow prediction results. Recently, consistency models, as a standalone generative network, have resolved this issue. Compared to diffusion models, consistency models can reduce the sampling times to once, not only achieving similar generative effects but also significantly speeding up training and prediction. However, they are not suitable for image segmentation tasks, and their application in the medical imaging field has not yet been explored. Therefore, this paper applies the consistency model to medical image segmentation tasks, designing multi-scale feature signal supervision modes and loss function guidance to achieve model convergence. Experiments have verified that the CTS model can obtain better medical image segmentation results with a single sampling during the test phase.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 19 of its 24 references that are in the map. First posted on arXiv 15 May 2024.

Builds on

19 of 24 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model1 Nov

Other references · 18

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2022Classifier-Free Diffusion Guidancecited by 5,717
  6. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,765
  7. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  8. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  9. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  10. 2022Diffusion-LM Improves Controllable Text Generationcited by 1,006
  11. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  12. 2021Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speechcited by 445
6 more
  1. 2022Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioningcited by 362
  2. 2022HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentationcited by 94
  3. 2017SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentationcited by 49
  4. 2022TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentationcited by 43
  5. 2022Can segmentation models be trained with fully synthetically generated data?cited by 37
  6. 2022Contextual Attention Network: Transformer Meets U-Netcited by 33

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  6. 2024Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models
  7. 2024Analysing Diffusion Segmentation for Medical Images
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