Towards frugal unsupervised detection of subtle abnormalities in medical imaging

• URL: http://arxiv.org/abs/2309.02458v1
• Date: Mon, 4 Sep 2023 07:44:54 GMT
• Title: Towards frugal unsupervised detection of subtle abnormalities in medical imaging
• Authors: Geoffroy Oudoumanessah (GIN, CREATIS, STATIFY), Carole Lartizien (CREATIS), Michel Dojat (GIN), Florence Forbes (STATIFY)
• Abstract summary: Anomaly detection in medical imaging is a challenging task in contexts where abnormalities are not annotated. We investigate mixtures of probability distributions whose versatility has been widely recognized. This online approach is illustrated on the challenging detection of subtle abnormalities in MR brain scans for the follow-up of newly diagnosed Parkinsonian patients.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection in medical imaging is a challenging task in contexts where abnormalities are not annotated. This problem can be addressed through unsupervised anomaly detection (UAD) methods, which identify features that do not match with a reference model of normal profiles. Artificial neural networks have been extensively used for UAD but they do not generally achieve an optimal trade-o$\hookleftarrow$ between accuracy and computational demand. As an alternative, we investigate mixtures of probability distributions whose versatility has been widely recognized for a variety of data and tasks, while not requiring excessive design e$\hookleftarrow$ort or tuning. Their expressivity makes them good candidates to account for complex multivariate reference models. Their much smaller number of parameters makes them more amenable to interpretation and e cient learning. However, standard estimation procedures, such as the Expectation-Maximization algorithm, do not scale well to large data volumes as they require high memory usage. To address this issue, we propose to incrementally compute inferential quantities. This online approach is illustrated on the challenging detection of subtle abnormalities in MR brain scans for the follow-up of newly diagnosed Parkinsonian patients. The identified structural abnormalities are consistent with the disease progression, as accounted by the Hoehn and Yahr scale.

Related papers

• Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection [88.34095233600719]
  FAPrompt is a novel framework designed to learn Fine-grained Abnormality Prompts for more accurate ZSAD. It substantially outperforms state-of-the-art methods by at least 3%-5% AUC/AP in both image- and pixel-level ZSAD tasks.
  arXiv  Detail & Related papers  (2024-10-14T08:41:31Z)
• Are we certain it's anomalous? [57.729669157989235]
  Anomaly detection in time series is a complex task since anomalies are rare due to highly non-linear temporal correlations. Here we propose the novel use of Hyperbolic uncertainty for Anomaly Detection (HypAD) HypAD learns self-supervisedly to reconstruct the input signal.
  arXiv  Detail & Related papers  (2022-11-16T21:31:39Z)
• Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles [52.77024349608834]
  We analyze the influence of replacing a DCNN with a state-of-the-art face recognition approach, iResNet with ArcFace. Our proposed ensemble model achieves state-of-the-art performance on both seen and unseen disorders.
  arXiv  Detail & Related papers  (2022-11-12T23:28:54Z)
• Augment to Detect Anomalies with Continuous Labelling [10.646747658653785]
  Anomaly detection is to recognize samples that differ in some respect from the training observations. Recent state-of-the-art deep learning-based anomaly detection methods suffer from high computational cost, complexity, unstable training procedures, and non-trivial implementation. We leverage a simple learning procedure that trains a lightweight convolutional neural network, reaching state-of-the-art performance in anomaly detection.
  arXiv  Detail & Related papers  (2022-07-03T20:11:51Z)
• Progressive GANomaly: Anomaly detection with progressively growing GANs [0.08122270502556374]
  Anomaly detection is a method that is capable of detecting unseen abnormal data while only being trained on normal data. This paper proposes a new method by combining an existing method, GANomaly, with progressively growing GANs. The method is tested using Fashion MNIST, Medical Out-of-Distribution Analysis Challenge (MOOD), and in-house brain MRI.
  arXiv  Detail & Related papers  (2022-06-08T13:13:01Z)
• StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder [48.2010192865749]
  Unsupervised anomaly detection (UAD) can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out of distribution samples. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA) The proposed pipeline achieved a Dice score of 0.642$pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$pm$0.112 while detecting artificially induced anomalies.
  arXiv  Detail & Related papers  (2022-01-31T14:27:35Z)
• Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection [0.0]
  Uncertainty is important in critical applications such as anomaly or lesion detection and clinical diagnosis. In this work, we focus on using quantile regression to estimate aleatoric uncertainty and use it for estimating uncertainty in both supervised and unsupervised lesion detection problems. We show how quantile regression can be used to characterize expert disagreement in the location of lesion boundaries.
  arXiv  Detail & Related papers  (2021-09-20T08:50:21Z)
• A Hamiltonian Monte Carlo Model for Imputation and Augmentation of Healthcare Data [0.6719751155411076]
  Missing values exist in nearly all clinical studies because data for a variable or question are not collected or not available. Existing models usually do not consider privacy concerns or do not utilise the inherent correlations across multiple features to impute the missing values. A Bayesian approach to impute missing values and creating augmented samples in high dimensional healthcare data is proposed in this work.
  arXiv  Detail & Related papers  (2021-03-03T11:57:42Z)
• Manifolds for Unsupervised Visual Anomaly Detection [79.22051549519989]
  Unsupervised learning methods that don't necessarily encounter anomalies in training would be immensely useful. We develop a novel hyperspherical Variational Auto-Encoder (VAE) via stereographic projections with a gyroplane layer. We present state-of-the-art results on visual anomaly benchmarks in precision manufacturing and inspection, demonstrating real-world utility in industrial AI scenarios.
  arXiv  Detail & Related papers  (2020-06-19T20:41:58Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

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.