Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. High annotation effort and the limitation to a vocabulary of known markers limit the power of such approaches. Here, we perform unsupervised learning to identify anomalies in imaging data as candidates for markers. We propose AnoGAN, a deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, accompanying a novel anomaly scoring scheme based on the mapping from image space to a latent space. Applied to new data, the model labels anomalies, and scores image patches indicating their fit into the learned distribution. Results on optical coherence tomography images of the retina demonstrate that the approach correctly identifies anomalous images, such as images containing retinal fluid or hyperreflective foci.

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

System Identification I

Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series. The methods examined include both supervised and unsupervised algorithms that have been introduced and evaluated. We introduce several criteria to compare the algorithms. Finally, we present some grand challenges for the community to consider.

A survey of methods for time series change point detection

The detection of anomalous structures in natural image data is of utmost importance for numerous tasks in the ﬁeld of computer vision. The development of methods for unsupervised anomaly detection requires data on which to train and evaluate new approaches and ideas. We introduce the MVTec Anomaly Detection (MVTec AD) dataset containing 5354 high-resolution color images of different object and texture categories. It contains normal, i.e., defect-free, images intended for training and images with anomalies intended for testing. The anomalies manifest themselves in the form of over 70 different types of defects such as scratches, dents, contaminations, and various structural changes. In addition, we provide pixel-precise ground truth regions for all anomalies. We also conduct a thorough evaluation of current state-of-the-art unsupervised anomaly detection methods based on deep architectures such as convolutional autoencoders, generative adversarial networks, and feature descriptors using pre-trained convolutional neural networks, as well as classical computer vision methods. This initial benchmark indicates that there is considerable room for improvement. To the best of our knowledge, this is the ﬁrst comprehensive, multi-object, multi-defect dataset for anomaly detection that provides pixel-accurate ground truth regions and focuses on real-world applications.

/pdf/mvtec-ad-a-comprehensive-real-world-dataset-for-unsupervised-3029ppuqb1.pdf

MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

Deep learning for pixel-level image fusion: Recent advances and future prospects

Nanoproducts represent a potential growing sector and nanofibrous materials are widely requested in industrial, medical, and environmental applications. Unfortunately, the production processes at the nanoscale are difficult to control and nanoproducts often exhibit localized defects that impair their functional properties. Therefore, defect detection is a particularly important feature in smart-manufacturing systems to raise alerts as soon as defects exceed a given tolerance level and to design production processes that both optimize the physical properties and control the defectiveness of the produced materials. Here, we present a novel solution to detect defects in nanofibrous materials by analyzing scanning electron microscope images. We employ an algorithm that learns, during a training phase, a model yielding sparse representations of the structures that characterize correctly produced nanofiborus materials. Defects are then detected by analyzing each patch of an input image and extracting features that quantitatively assess whether the patch conforms or not to the learned model. The proposed solution has been successfully validated over 45 images acquired from samples produced by a prototype electrospinning machine. The low computational times indicate that the proposed solution can be effectively adopted in a monitoring system for industrial production.

/pdf/defect-detection-in-sem-images-of-nanofibrous-materials-4hpytvel4c.pdf

Defect Detection in SEM Images of Nanofibrous Materials

We address the problem of detecting anomalies in images, specifically that of detecting regions characterized by structures that do not conform those of normal images In the proposed approach we exploit convolutional sparse models to learn a dictionary of filters from a training set of normal images These filters capture the structure of normal images and are leveraged to quantitatively assess whether regions of a test image are normal or anomalous Each test image is at first encoded with respect to the learned dictionary, yielding sparse coefficient maps, and then analyzed by computing indicator vectors that assess the conformance of local image regions with the learned filters Anomalies are then detected by identifying outliers in these indicators Our experiments demonstrate that a convolutional sparse model provides better anomaly-detection performance than an equivalent method based on standard patch-based sparsity Most importantly, our results highlight that monitoring the local group sparsity, namely the spread of nonzero coefficients across different maps, is essential for detecting anomalous regions

/pdf/detecting-anomalous-structures-by-convolutional-sparse-1jd8zggusb.pdf

Detecting anomalous structures by convolutional sparse models

https://re.public.polimi.it/bitstream/11311/1126166/1/2019_PR_ECG.pdf

Online anomaly detection for long-term ECG monitoring using wearable devices

We address the problem of automatically detecting anomalies in images, ie, patterns that do not conform to those appearing in a reference training set This is a very important feature for enabling an intelligent system to autonomously check the validity of acquired data, thus performing a preliminary, automatic, diagnosis We approach this problem in a patch-wise manner, by learning a model to represent patches belonging to a training set of normal images Here, we consider a model based on sparse representations, and we show that jointly monitoring the sparsity and the reconstruction error of such representation substantially improves the detection performance with respect to other approaches leveraging sparse models As an illustrative application, we consider the detection of anomalies in scanning electron microscope (SEM) images, which is essential for supervising the production of nanofibrous materials

Novelty detection in images by sparse representations

We address the problem of detecting distribution changes in multivariate data streams by means of histograms. Histograms are very general and flexible models, which have been relatively ignored in the change-detection literature as they often require a number of bins that grows unfeasibly with the data dimension. We present QuantTree, a recursive binary splitting scheme that adaptively defines the histogram bins to ease the detection of any distribution change. Our design scheme implies that i) we can easily control the overall number of bins and ii) the bin probabilities do not depend on the distribution of stationary data. This latter is a very relevant aspect in change detection, since thresholds of tests statistics based on these histograms (e.g., the Pearson statistic or the total variation) can be numerically computed from univariate and synthetically generated data, yet guaranteeing a controlled false positive rate. Our experiments show that the proposed histograms are very effective in detecting changes in high dimensional data streams, and that the resulting thresholds can effectively control the false positive rate, even when the number of training samples is relatively small.

/pdf/quanttree-histograms-for-change-detection-in-multivariate-24p3p4um4h.pdf

Diego Carrera

Papers

Defect Detection in SEM Images of Nanofibrous Materials

Detecting anomalous structures by convolutional sparse models

Online anomaly detection for long-term ECG monitoring using wearable devices

Novelty detection in images by sparse representations

QuantTree: Histograms for change detection in multivariate data streams