Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models

by   Walter H. L Pinaya, et al.

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 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.


The role of noise in denoising models for anomaly detection in medical images

Pathological brain lesions exhibit diverse appearance in brain images, i...

CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization

Unsupervised anomaly detection in medical imaging aims to detect and loc...

Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection

Early and accurate disease detection is crucial for patient management a...

Generative AI for Medical Imaging: extending the MONAI Framework

Recent advances in generative AI have brought incredible breakthroughs i...

Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI

Unsupervised anomaly segmentation aims to detect patterns that are disti...

Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models

The introduction of diffusion models in anomaly detection has paved the ...

Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images

Segmentation masks of pathological areas are useful in many medical appl...

Please sign up or login with your details

Forgot password? Click here to reset