Denoising diffusion models for out-of-distribution detection

Graham, Mark S.; Pinaya, Walter Hugo Lopez; Tudosiu, Petru-Daniel; Nachev, Parashkev; Ourselin, Sébastien; Cardoso, M. Jorge

doi:10.48550/arxiv.2211.07740

Cited by 1 publication

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“…Recent developments in diffusion models have addressed these challenges through various approaches, including adapting the noise distribution to match the target anomaly distribution (Wyatt et al, 2022), employing context information to enhance the de-noising process (Behrendt et al, 2023), exploiting the characteristic of noise selection in diffusion models for out-of-distribution detection (Graham et al, 2022), or incorporating classifier guidance to augment the detection and restoration process (Wolleb et al, 2022). Recently, Bercea et al (2023b) introduced an unsupervised automatic in-painting pipeline for anomaly detection.…”

Section: Introductionmentioning

confidence: 99%

Federated Disentangled Representation Learning for Unsupervised Brain Anomaly Detection

Bercea

Wiestler²,

Rueckert

et al. 2021

Preprint

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Recent advances in Deep Learning (DL) and the increased use of brain MRI have provided a great opportunity and interest in automated anomaly segmentation to support human interpretation and improve clinical workflow. However, medical imaging must be curated by trained clinicians, which is time-consuming and expensive. Further, data is often scattered across multiple institutions, with privacy regulations limiting its access. Here, we present FedDis (Federated Disentangled representation learning for unsupervised brain pathology segmentation) to collaboratively train an unsupervised deep convolutional neural network on 1532 healthy MR scans from four different institutions, and evaluate its performance in identifying abnormal brain MRIs including multiple sclerosis (MS) lesions, low-grade tumors (LGG), and high-grade tumors/glioblastoma (HGG/GB) on a total of ~500 scans from 5 different institutions and datasets. FedDis mitigates the statistical heterogeneity given by different scanners by disentangling the parameter space into global, i.e., shape and local, i.e., appearance. We only share the former with the federated clients to leverage common anatomical structure while keeping client-specific contrast information private. We have shown that our collaborative approach, FedDis, improves anomaly segmentation results by 99.74% for MS and 40.45% for tumors over locally trained models without the need for annotations or sharing private local data. We found out that FedDis is especially beneficial for clients that share both healthy and anomaly data coming from the same institute, improving their local anomaly detection performance by up to 227% for MS lesions and 77% for brain tumors.

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