529 research outputs found
Towards Universal Unsupervised Anomaly Detection in Medical Imaging
The increasing complexity of medical imaging data underscores the need for
advanced anomaly detection methods to automatically identify diverse
pathologies. Current methods face challenges in capturing the broad spectrum of
anomalies, often limiting their use to specific lesion types in brain scans. To
address this challenge, we introduce a novel unsupervised approach, termed
\textit{Reversed Auto-Encoders (RA)}, designed to create realistic
pseudo-healthy reconstructions that enable the detection of a wider range of
pathologies. We evaluate the proposed method across various imaging modalities,
including magnetic resonance imaging (MRI) of the brain, pediatric wrist X-ray,
and chest X-ray, and demonstrate superior performance in detecting anomalies
compared to existing state-of-the-art methods. Our unsupervised anomaly
detection approach may enhance diagnostic accuracy in medical imaging by
identifying a broader range of unknown pathologies. Our code is publicly
available at: \url{https://github.com/ci-ber/RA}
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art
Deep unsupervised approaches are gathering increased attention for
applications such as pathology detection and segmentation in medical images
since they promise to alleviate the need for large labeled datasets and are
more generalizable than their supervised counterparts in detecting any kind of
rare pathology. As the Unsupervised Anomaly Detection (UAD) literature
continuously grows and new paradigms emerge, it is vital to continuously
evaluate and benchmark new methods in a common framework, in order to reassess
the state-of-the-art (SOTA) and identify promising research directions. To this
end, we evaluate a diverse selection of cutting-edge UAD methods on multiple
medical datasets, comparing them against the established SOTA in UAD for brain
MRI. Our experiments demonstrate that newly developed feature-modeling methods
from the industrial and medical literature achieve increased performance
compared to previous work and set the new SOTA in a variety of modalities and
datasets. Additionally, we show that such methods are capable of benefiting
from recently developed self-supervised pre-training algorithms, further
increasing their performance. Finally, we perform a series of experiments in
order to gain further insights into some unique characteristics of selected
models and datasets. Our code can be found under
https://github.com/iolag/UPD_study/.Comment: 12 pages, 4 figures, accepted for publication in IEEE Transactions on
Medical Imaging (added copyright, DOI information
MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images
Unsupervised Out-of-Distribution (OOD) detection consists in identifying
anomalous regions in images leveraging only models trained on images of healthy
anatomy. An established approach is to tokenize images and model the
distribution of tokens with Auto-Regressive (AR) models. AR models are used to
1) identify anomalous tokens and 2) in-paint anomalous representations with
in-distribution tokens. However, AR models are slow at inference time and prone
to error accumulation issues which negatively affect OOD detection performance.
Our novel method, MIM-OOD, overcomes both speed and error accumulation issues
by replacing the AR model with two task-specific networks: 1) a transformer
optimized to identify anomalous tokens and 2) a transformer optimized to
in-paint anomalous tokens using masked image modelling (MIM). Our experiments
with brain MRI anomalies show that MIM-OOD substantially outperforms AR models
(DICE 0.458 vs 0.301) while achieving a nearly 25x speedup (9.5s vs 244s).Comment: 12 pages, 5 figures. Accepted in DGM4MICCAI workshop @ MICCAI 202
ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction
Most advanced unsupervised anomaly detection (UAD) methods rely on modeling
feature representations of frozen encoder networks pre-trained on large-scale
datasets, e.g. ImageNet. However, the features extracted from the encoders that
are borrowed from natural image domains coincide little with the features
required in the target UAD domain, such as industrial inspection and medical
imaging. In this paper, we propose a novel epistemic UAD method, namely
ReContrast, which optimizes the entire network to reduce biases towards the
pre-trained image domain and orients the network in the target domain. We start
with a feature reconstruction approach that detects anomalies from errors.
Essentially, the elements of contrastive learning are elegantly embedded in
feature reconstruction to prevent the network from training instability,
pattern collapse, and identical shortcut, while simultaneously optimizing both
the encoder and decoder on the target domain. To demonstrate our transfer
ability on various image domains, we conduct extensive experiments across two
popular industrial defect detection benchmarks and three medical image UAD
tasks, which shows our superiority over current state-of-the-art methods.Comment: NeurIPS 2023 Poste
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