Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models
<|reference_start|>Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models: Congenital malformations of the brain are among the most common fetal abnormalities that impact fetal development. Previous anomaly detection methods on ultrasound images are based on supervised learning, rely on manual annotations, and risk missing underrepresented categories. In this work, we frame fetal brain anomaly detection as an unsupervised task using diffusion models. To this end, we employ an inpainting-based Noise Agnostic Anomaly Detection approach that identifies the abnormality using diffusion-reconstructed fetal brain images from multiple noise levels. Our approach only requires normal fetal brain ultrasound images for training, addressing the limited availability of abnormal data. Our experiments on a real-world clinical dataset show the potential of using unsupervised methods for fetal brain anomaly detection. Additionally, we comprehensively evaluate how different noise types affect diffusion models in the fetal anomaly detection domain.<|reference_end|>
@article{olsen2024unsupervised,
    title={Unsupervised Detection of Fetal Brain Anomalies using Denoising
  Diffusion Models},
    author={Markus Ditlev Sj{o}gren Olsen, Jakob Ambsdorf, Manxi Lin, Caroline
  Taks{o}e-Vester, Morten Bo S{o}ndergaard Svendsen, Anders Nymark
  Christensen, Mads Nielsen, Martin Gr{o}nneb{ae}k Tolsgaard, Aasa Feragen,
  Paraskevas Pegios},
    journal={arXiv preprint arXiv:2408.03654},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03654},
    primaryClass={eess.IV cs.CV}
}