mlsanityData to 7 Oct 2026

Diff-Lung: Diffusion-Based Texture Synthesis for Enhanced Pathological Tissue Segmentation in Lung CT Scans

arXiv 2501.02867 ↗First posted 6 Jan 2025eess.IV cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Accurate quantification of the extent of lung pathological patterns (fibrosis, ground-glass opacity, emphysema, consolidation) is prerequisite for diagnosis and follow-up of interstitial lung diseases. However, segmentation is challenging due to the significant class imbalance between healthy and pathological tissues. This paper addresses this issue by leveraging a diffusion model for data augmentation applied during training an AI model. Our approach generates synthetic pathological tissue patches while preserving essential shape characteristics and intricate details specific to each tissue type. This method enhances the segmentation process by increasing the occurence of underrepresented classes in the training data. We demonstrate that our diffusion-based augmentation technique improves segmentation accuracy across all pathological tissue types, particularly for the less common patterns. This advancement contributes to more reliable automated analysis of lung CT scans, potentially improving clinical decision-making and patient outcomes

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 11 of its 21 references that are in the map. First posted on arXiv 6 Jan 2025.

Builds on

11 of 21 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models7 Feb

Other references · 10

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2022Generative Adversarial Networkscited by 5,603
  5. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  6. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,295
  7. 2018Which Training Methods for GANs do actually Converge?cited by 672
  8. 2015Cyclical Learning Rates for Training Neural Networkscited by 627
  9. 2019Lookahead Optimizer: k steps forward, 1 step backcited by 333
  10. 2020CT Image Segmentation for Inflamed and Fibrotic Lungs Using a Multi-Resolution Convolutional Neural Networkcited by 4

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026DiffSegLung: Diffusion Radiomic Distillation for Unsupervised Lung Pathology Segmentation
  2. 2026Controllable Lung Nodule Synthesis via Histogram-Regularized Latent Diffusion Models
  3. 2023High-Fidelity Image Synthesis from Pulmonary Nodule Lesion Maps using Semantic Diffusion Model
  4. 2025Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis
  5. 2024Evaluating Utility of Memory Efficient Medical Image Generation: A Study on Lung Nodule Segmentation
  6. 2024Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation
  7. 2023Latent Diffusion Models with Image-Derived Annotations for Enhanced AI-Assisted Cancer Diagnosis in Histopathology
  8. 2025Lung-DDPM+: Efficient Thoracic CT Image Synthesis using Diffusion Probabilistic Model