Bias in Unsupervised Anomaly Detection in Brain MRI

• URL: http://arxiv.org/abs/2308.13861v1
• Date: Sat, 26 Aug 2023 12:39:25 GMT
• Title: Bias in Unsupervised Anomaly Detection in Brain MRI
• Authors: Cosmin I. Bercea, Esther Puyol-Ant\'on, Benedikt Wiestler, Daniel Rueckert, Julia A. Schnabel, Andrew P. King
• Abstract summary: Unsupervised anomaly detection methods offer a promising and flexible alternative to supervised approaches. In the current landscape, it is commonly assumed that differences between a test case and the training distribution are attributed solely to pathological conditions. The presence of other potential sources of distributional shift, including scanner, age, sex, or race, is frequently overlooked.
• Score: 12.857583780524847
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Unsupervised anomaly detection methods offer a promising and flexible alternative to supervised approaches, holding the potential to revolutionize medical scan analysis and enhance diagnostic performance. In the current landscape, it is commonly assumed that differences between a test case and the training distribution are attributed solely to pathological conditions, implying that any disparity indicates an anomaly. However, the presence of other potential sources of distributional shift, including scanner, age, sex, or race, is frequently overlooked. These shifts can significantly impact the accuracy of the anomaly detection task. Prominent instances of such failures have sparked concerns regarding the bias, credibility, and fairness of anomaly detection. This work presents a novel analysis of biases in unsupervised anomaly detection. By examining potential non-pathological distributional shifts between the training and testing distributions, we shed light on the extent of these biases and their influence on anomaly detection results. Moreover, this study examines the algorithmic limitations that arise due to biases, providing valuable insights into the challenges encountered by anomaly detection algorithms in accurately learning and capturing the entire range of variability present in the normative distribution. Through this analysis, we aim to enhance the understanding of these biases and pave the way for future improvements in the field. Here, we specifically investigate Alzheimer's disease detection from brain MR imaging as a case study, revealing significant biases related to sex, race, and scanner variations that substantially impact the results. These findings align with the broader goal of improving the reliability, fairness, and effectiveness of anomaly detection in medical imaging.

Related papers

• Can I trust my anomaly detection system? A case study based on explainable AI [0.4416503115535552]
  This case study explores the robustness of an anomaly detection system based on variational autoencoder generative models. The goal is to get a different perspective on the real performances of anomaly detectors that use reconstruction differences.
  arXiv  Detail & Related papers  (2024-07-29T12:39:07Z)
• Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET [0.0]
  Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging data. It relies on building a subject-specific model of healthy appearance to which a subject's image can be compared to detect anomalies. In the literature, it is common for anomaly detection to rely on analysing the residual image between the subject's image and its pseudo-healthy reconstruction.
  arXiv  Detail & Related papers  (2023-11-20T10:28:10Z)
• Precursor-of-Anomaly Detection for Irregular Time Series [31.73234935455713]
  We present a novel type of anomaly detection, called Precursor-of-Anomaly (PoA) detection. To solve both problems at the same time, we present a neural controlled differential equation-based neural network and its multi-task learning algorithm.
  arXiv  Detail & Related papers  (2023-06-27T14:10:09Z)
• SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection [24.43321988051129]
  We propose a novel saliency-guided data augmentation method, SaliencyCut, to produce pseudo but more common anomalies. We then design a novel patch-wise residual module in the anomaly learning head to extract and assess the fine-grained anomaly features from each sample.
  arXiv  Detail & Related papers  (2023-06-14T08:55:36Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Prototypical Residual Networks for Anomaly Detection and Localization [80.5730594002466]
  We propose a framework called Prototypical Residual Network (PRN) PRN learns feature residuals of varying scales and sizes between anomalous and normal patterns to accurately reconstruct the segmentation maps of anomalous regions. We present a variety of anomaly generation strategies that consider both seen and unseen appearance variance to enlarge and diversify anomalies.
  arXiv  Detail & Related papers  (2022-12-05T05:03:46Z)
• Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection [90.32910087103744]
  A few labeled anomaly examples are often available in many real-world applications. These anomaly examples provide valuable knowledge about the application-specific abnormality. Those anomalies seen during training often do not illustrate every possible class of anomaly. This paper tackles open-set supervised anomaly detection.
  arXiv  Detail & Related papers  (2022-03-28T05:21:37Z)
• Explainable Deep Few-shot Anomaly Detection with Deviation Networks [123.46611927225963]
  We introduce a novel weakly-supervised anomaly detection framework to train detection models. The proposed approach learns discriminative normality by leveraging the labeled anomalies and a prior probability. Our model is substantially more sample-efficient and robust, and performs significantly better than state-of-the-art competing methods in both closed-set and open-set settings.
  arXiv  Detail & Related papers  (2021-08-01T14:33:17Z)
• Understanding the Effect of Bias in Deep Anomaly Detection [15.83398707988473]
  Anomaly detection presents a unique challenge in machine learning, due to the scarcity of labeled anomaly data. Recent work attempts to mitigate such problems by augmenting training of deep anomaly detection models with additional labeled anomaly samples. In this paper, we aim to understand the effect of a biased anomaly set on anomaly detection.
  arXiv  Detail & Related papers  (2021-05-16T03:55:02Z)
• The Hidden Uncertainty in a Neural Networks Activations [105.4223982696279]
  The distribution of a neural network's latent representations has been successfully used to detect out-of-distribution (OOD) data. This work investigates whether this distribution correlates with a model's epistemic uncertainty, thus indicating its ability to generalise to novel inputs.
  arXiv  Detail & Related papers  (2020-12-05T17:30:35Z)
• Deep Weakly-supervised Anomaly Detection [118.55172352231381]
  Pairwise Relation prediction Network (PReNet) learns pairwise relation features and anomaly scores. PReNet can detect any seen/unseen abnormalities that fit the learned pairwise abnormal patterns. Empirical results on 12 real-world datasets show that PReNet significantly outperforms nine competing methods in detecting seen and unseen anomalies.
  arXiv  Detail & Related papers  (2019-10-30T00:40:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.