mlsanityData to 14 Oct 2026

Semantic Map Guided Synthesis of Wireless Capsule Endoscopy Images using Diffusion Models

arXiv 2311.05889 ↗First posted 10 Nov 2023eess.IV cs.CV cs.LGcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Wireless capsule endoscopy (WCE) is a non-invasive method for visualizing the gastrointestinal (GI) tract, crucial for diagnosing GI tract diseases. However, interpreting WCE results can be time-consuming and tiring. Existing studies have employed deep neural networks (DNNs) for automatic GI tract lesion detection, but acquiring sufficient training examples, particularly due to privacy concerns, remains a challenge. Public WCE databases lack diversity and quantity. To address this, we propose a novel approach leveraging generative models, specifically the diffusion model (DM), for generating diverse WCE images. Our model incorporates semantic map resulted from visualization scale (VS) engine, enhancing the controllability and diversity of generated images. We evaluate our approach using visual inspection and visual Turing tests, demonstrating its effectiveness in generating realistic and diverse WCE images.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 15 of its 33 references that are in the map. First posted on arXiv 10 Nov 2023.

Builds on

15 of 33 references.

References · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  7. 2022Generative Adversarial Networkscited by 5,627
  8. 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
  9. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,114
  10. 2017Data Augmentation Generative Adversarial Networkscited by 278
  11. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  12. 2023This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generationcited by 9
3 more
  1. 2022This changes to that : Combining causal and non-causal explanations to generate disease progression in capsule endoscopycited by 8
  2. 2023Evaluating clinical diversity and plausibility of synthetic capsule endoscopic imagescited by 7
  3. 2020Deep generative models for musical audio synthesiscited by 6

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  1. 2023LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion
  2. 2025Multiscale Vector-Quantized Variational Autoencoder for Endoscopic Image Synthesis
  3. 2026CapCLIP: A Vision-Language Representation Alignment Approach for Wireless Capsule Endoscopy Analysis
  4. 2026Anatomical Landmark-Guided Deep Reinforcement Learning for Autonomous Gastric Navigation ↗
  5. 2026Representation-driven Endoscopic Visual Embedding Alignment for Latent Generation
  6. 2025Super-Resolution Enhancement of Medical Images Based on Diffusion Model: An Optimization Scheme for Low-Resolution Gastric Images
  7. 2026GALAR-TemporalNet v2: Anatomy-Guided Dual-Branch Temporal Classification with Bidirectional Mamba and Dual-Graph GCN for Video Capsule Endoscopy -- after competition results
  8. 2025CapsDT: Diffusion-Transformer for Capsule Robot Manipulation