88 research outputs found
Scalable anomaly detection in manufacturing systems using an interpretable deep learning approach
Anomaly detection in manufacturing systems has great potential for the prevention of critical quality faults. In recent years, unsupervised deep learning has shown to frequently outperform conventional methods for anomaly detection. However, tuning, deploying and debugging deep learning models is a time-consuming task, limiting their practical applicability in manufacturing systems. We approach this problem by developing a deep learning model that learns interpretable shapes that can be used for anomaly detection in temporal process data. Application of the model to assembly tightening processes in the automotive industry shows a significant improvement in model interpretability and scalability
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features
One-class support vector machine (OC-SVM) for a long time has been one of the
most effective anomaly detection methods and extensively adopted in both
research as well as industrial applications. The biggest issue for OC-SVM is
yet the capability to operate with large and high-dimensional datasets due to
optimization complexity. Those problems might be mitigated via dimensionality
reduction techniques such as manifold learning or autoencoder. However,
previous work often treats representation learning and anomaly prediction
separately. In this paper, we propose autoencoder based one-class support
vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier
features to approximate the radial basis kernel, into deep learning context by
combining it with a representation learning architecture and jointly exploit
stochastic gradient descent to obtain end-to-end training. Interestingly, this
also opens up the possible use of gradient-based attribution methods to explain
the decision making for anomaly detection, which has ever been challenging as a
result of the implicit mappings between the input space and the kernel space.
To the best of our knowledge, this is the first work to study the
interpretability of deep learning in anomaly detection. We evaluate our method
on a wide range of unsupervised anomaly detection tasks in which our end-to-end
training architecture achieves a performance significantly better than the
previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201
Backpropagated Gradient Representations for Anomaly Detection
Learning representations that clearly distinguish between normal and abnormal
data is key to the success of anomaly detection. Most of existing anomaly
detection algorithms use activation representations from forward propagation
while not exploiting gradients from backpropagation to characterize data.
Gradients capture model updates required to represent data. Anomalies require
more drastic model updates to fully represent them compared to normal data.
Hence, we propose the utilization of backpropagated gradients as
representations to characterize model behavior on anomalies and, consequently,
detect such anomalies. We show that the proposed method using gradient-based
representations achieves state-of-the-art anomaly detection performance in
benchmark image recognition datasets. Also, we highlight the computational
efficiency and the simplicity of the proposed method in comparison with other
state-of-the-art methods relying on adversarial networks or autoregressive
models, which require at least 27 times more model parameters than the proposed
method.Comment: European Conference on Computer Vision (ECCV) 202
Anomaly Detection for Vision-based Railway Inspection
none7nomixedRiccardo Gasparini; Stefano Pini; Guido Borghi; Giuseppe Scaglione; Simone Calderara; Eugenio Fedeli; Rita CucchiaraRiccardo Gasparini; Stefano Pini; Guido Borghi; Giuseppe Scaglione; Simone Calderara; Eugenio Fedeli; Rita Cucchiar
GAN-based multiple adjacent brain MRI slice reconstruction for unsupervised alzheimer’s disease diagnosis
Unsupervised learning can discover various unseen diseases, relying on
large-scale unannotated medical images of healthy subjects. Towards this,
unsupervised methods reconstruct a single medical image to detect outliers
either in the learned feature space or from high reconstruction loss. However,
without considering continuity between multiple adjacent slices, they cannot
directly discriminate diseases composed of the accumulation of subtle
anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has
shown how unsupervised anomaly detection is associated with disease stages.
Therefore, we propose a two-step method using Generative Adversarial
Network-based multiple adjacent brain MRI slice reconstruction to detect AD at
various stages: (Reconstruction) Wasserstein loss with Gradient Penalty + L1
loss---trained on 3 healthy slices to reconstruct the next 3
ones---reconstructs unseen healthy/AD cases; (Diagnosis) Average/Maximum loss
(e.g., L2 loss) per scan discriminates them, comparing the reconstructed/ground
truth images. The results show that we can reliably detect AD at a very early
stage with Area Under the Curve (AUC) 0.780 while also detecting AD at a late
stage much more accurately with AUC 0.917; since our method is fully
unsupervised, it should also discover and alert any anomalies including rare
disease.Comment: 10 pages, 4 figures, Accepted to Lecture Notes in Bioinformatics
(LNBI) as a volume in the Springer serie
Weakly-Supervised Evidence Pinpointing and Description
We propose a learning method to identify which specific regions and features of images contribute to a certain classification. In the medical imaging context, they can be the evidence regions where the abnormalities are most likely to appear, and the discriminative features of these regions supporting the pathology classification. The learning is weakly-supervised requiring only the pathological labels and no other prior knowledge. The method can also be applied to learn the salient description of an anatomy discriminative from its background, in order to localise the anatomy before a classification step. We formulate evidence pinpointing as a sparse descriptor learning problem. Because of the large computational complexity, the objective function is composed in a stochastic way and is optimised by the Regularised Dual Averaging algorithm. We demonstrate that the learnt feature descriptors contain more specific and better discriminative information than hand-crafted descriptors contributing to superior performance for the tasks of anatomy localisation and pathology classification respectively. We apply our method on the problem of lumbar spinal stenosis for localising and classifying vertebrae in MRI images. Experimental results show that our method when trained with only target labels achieves better or competitive performance on both tasks compared with strongly-supervised methods requiring labels and multiple landmarks. A further improvement is achieved with training on additional weakly annotated data, which gives robust localisation with average error within 2 mm and classification accuracies close to human performance
Building ProteomeTools based on a complete synthetic human proteome.
We describe ProteomeTools, a project building molecular and digital tools from the human proteome to facilitate biomedical research. Here we report the generation and multimodal liquid chromatography-tandem mass spectrometry analysis of \u3e330,000 synthetic tryptic peptides representing essentially all canonical human gene products, and we exemplify the utility of these data in several applications. The resource (available at http://www.proteometools.org) will be extended to \u3e1 million peptides, and all data will be shared with the community via ProteomicsDB and ProteomeXchange
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