mlsanityData to 14 Oct 2026

Masked Conditional Diffusion Models for Image Analysis with Application to Radiographic Diagnosis of Infant Abuse

arXiv 2311.13688 ↗First posted 22 Nov 2023eess.IV cs.CV cs.LGcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

The classic metaphyseal lesion (CML) is a distinct injury that is highly specific for infant abuse. It commonly occurs in the distal tibia. To aid radiologists detect these subtle fractures, we need to develop a model that can flag abnormal distal tibial radiographs (i.e. those with CMLs). Unfortunately, the development of such a model requires a large and diverse training database, which is often not available. To address this limitation, we propose a novel generative model for data augmentation. Unlike previous models that fail to generate data that span the diverse radiographic appearance of the distal tibial CML, our proposed masked conditional diffusion model (MaC-DM) not only generates realistic-appearing and wide-ranging synthetic images of the distal tibial radiographs with and without CMLs, it also generates their associated segmentation labels. To achieve these tasks, MaC-DM combines the weighted segmentation masks of the tibias and the CML fracture sites as additional conditions for classifier guidance. The augmented images from our model improved the performances of ResNet-34 in classifying normal radiographs and those with CMLs. Further, the augmented images and their associated segmentation masks enhanced the performance of the U-Net in labeling areas of the CMLs on distal tibial radiographs.

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 13 of its 24 references that are in the map. First posted on arXiv 22 Nov 2023.

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13 of 24 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Diffusion Models for Medical Anomaly Detection8 Mar
  2. 2022Towards performant and reliable undersampled MR reconstruction via diffusion model sampling8 Mar

Other references · 11

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  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. 2020Denoising Diffusion Implicit Modelscited by 9,781
  6. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  7. 2022Generative Adversarial Networkscited by 5,627
  8. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  9. 2019AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adversarial Networkscited by 77
  10. 2022Diffusion Deformable Model for 4D Temporal Medical Image Generationcited by 65
  11. 2020MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstructioncited by 50

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  6. 2023Surf-CDM: Score-Based Surface Cold-Diffusion Model For Medical Image Segmentation
  7. 2022Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains
  8. 2023Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation