Pinaya, WHL;
Graham, MS;
Gray, R;
da Costa, PF;
Tudosiu, PD;
Wright, P;
Mah, YH;
... Cardoso, MJ; + view all
(2022)
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models.
In:
International Conference on Medical Image Computing and Computer-Assisted Intervention : Medical Image Computing and Computer Assisted Intervention – MICCAI 2022.
(pp. pp. 705-714).
Springer, Cham
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Abstract
Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling. Recently, autoregressive transformers have achieved state-of-the-art performance for anomaly detection in medical imaging. Nonetheless, these models still have some intrinsic weaknesses, such as requiring images to be modelled as 1D sequences, the accumulation of errors during the sampling process, and the significant inference times associated with transformers. Denoising diffusion probabilistic models are a class of non-autoregressive generative models recently shown to produce excellent samples in computer vision (surpassing Generative Adversarial Networks), and to achieve log-likelihoods that are competitive with transformers while having relatively fast inference times. Diffusion models can be applied to the latent representations learnt by autoencoders, making them easily scalable and great candidates for application to high dimensional data, such as medical images. Here, we propose a method based on diffusion models to detect and segment anomalies in brain imaging. By training the models on healthy data and then exploring its diffusion and reverse steps across its Markov chain, we can identify anomalous areas in the latent space and hence identify anomalies in the pixel space. Our diffusion models achieve competitive performance compared with autoregressive approaches across a series of experiments with 2D CT and MRI data involving synthetic and real pathological lesions with much reduced inference times, making their usage clinically viable.
| Type: | Proceedings paper |
|---|---|
| Title: | Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models |
| Event: | MICCAI 2022 |
| ISBN-13: | 9783031164514 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-16452-1_67 |
| Publisher version: | https://doi.org/10.1007/978-3-031-16452-1_67 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Denoising Diffusion Probabilistic Models· Unsupervised Anomaly Detection· Out-of-Distribution Detection· Lesion segmentation · Neuroimaging |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10158253 |
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