Sampling for Deep Learning Model Diagnosis (Technical Report)
University Of Washington（华盛顿大学）；Paul Allen School of Computer Science and Engineering（保罗·艾伦计算机科学与工程学院）；DiRAC Institute（DiRAC研究所）；Department of Astronomy（天文学系）
Parmita Mehta, Stephen Portillo, Magdalena Balazinska, Andrew Connolly
Deep learning (DL) models have achieved paradigm-changing performance in many fields with high dimensional data, such as images, audio, and text. However, the black-box nature of deep neural networks is a barrier not just to adoption in applications such as medical diagnosis, where interpretability is essential, but also impedes diagnosis of under performing models. The task of diagnosing or explaining DL models requires the computation of additional artifacts, such as activation values and gradients. These artifacts are large in volume, and their computation, storage, and querying raise significant data management challenges.
In this paper, we articulate DL diagnosis as a data management problem, and we propose a general, yet representative, set of queries to evaluate systems that strive to support this new workload. We further develop a novel data sampling technique that produce approximate but accurate results for these model debugging queries. Our sampling technique utilizes the lower dimension representation learned by the DL model and focuses on model decision boundaries for the data in this lower dimensional space. We evaluate our techniques on one standard computer vision and one scientific data set and demonstrate that our sampling technique outperforms a variety of state-of-the-art alternatives in terms of query accuracy.
SKF Research & Technology Development（斯凯孚研究与技术开发部）；SKF Condition Monitoring Center Luleå AB（斯凯孚吕勒奥状态监测中心有限公司）；Luleå University of Technology（吕勒奥理工大学）
In the process industry, condition monitoring systems with automated fault diagnosis methods assist human experts and thereby improve maintenance efficiency, process sustainability, and workplace safety. Improving the automated fault diagnosis methods using data and machine learning-based models is a central aspect of intelligent fault diagnosis (IFD). A major challenge in IFD is to develop realistic datasets with accurate labels needed to train and validate models, and to transfer models trained with labeled lab data to heterogeneous process industry environments. However, fault descriptions …
German Research Center for Artificial Intelligence（德国人工智能研究中心）
A shortage of dermatologists causes long wait times for patients who seek dermatologic care. In addition, the diagnostic accuracy of general practitioners has been reported to be lower than the accuracy of artificial intelligence software. This article describes the Skincare project (H2020, EIT Digital). Contributions include enabling technology for clinical decision support based on interactive machine learning (IML), a reference architecture towards a Digital European Healthcare Infrastructure (also cf. EIT MCPS), technical components for aggregating digitised patient information, and the in…
ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Data
Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, College of Computer Science, Zhejiang University（浙江可感知与智能系统重点实验室，计算机科学学院，浙江大学）；The Zhejiang University-University of Illinois Urbana-Champaign Institute（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合研究所）
University Hospital Aachen（亚琛大学医院）；RWTH Aachen University（亚琛工业大学）；Ocumeda GmbH（Ocumeda 有限公司）；RWTH Aachen（亚琛工业大学）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Technical University Dresden（德累斯顿工业大学）；University of Leeds（利兹大学）；University Hospital Heidelberg（海德堡大学医院）
Multimodal deep learning has been used to predict clinical endpoints and diagnoses from clinical routine data. However, these models suffer from scaling issues: they have to learn pairwise interactions between each piece of information in each data type, thereby escalating model complexity beyond manageable scales. This has so far precluded a widespread use of multimodal deep learning. Here, we present a new technical approach of "learnable synergies", in which the model only selects relevant interactions between data modalities and keeps an "internal memory" of relevant data. Our approach is …
Elbow fractures are one of the most common fracture types. Diagnoses on elbow fractures often need the help of radiographic imaging to be read and analyzed by a specialized radiologist with years of training. Thanks to the recent advances of deep learning, a model that can classify and detect different types of bone fractures needs only hours of training and has shown promising results. However, most existing deep learning models are purely data-driven, lacking incorporation of known domain knowledge from human experts. In this work, we propose a novel deep learning method to diagnose elbow fr…
Northeast Electric Power University（东北电力大学）；Technical University of Denmark（丹麦技术大学）
A fault diagnosis method for power electronics converters based on deep feedforward network and wavelet compression is proposed in this paper. The transient historical data after wavelet compression are used to realize the training of fault diagnosis classifier. Firstly, the correlation analysis of the voltage or current data running in various fault states is performed to remove the redundant features and the sampling point. Secondly, the wavelet transform is used to remove the redundant data of the features, and then the training sample data is greatly compressed. The deep feedforward networ…
Isik University（伊希克大学）；Istanbul Technical University（伊斯坦布尔技术大学）
Retinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) are promising tools for the (early) diagnosis of Alzheimer's disease (AD). These non-invasive imaging techniques are cost-effective and more accessible than alternative neuroimaging tools. However, interpreting and classifying multi-slice scans produced by OCT devices is time-consuming and challenging even for trained practitioners. There are surveys on machine learning and deep learning approaches concerning the automated analysis of OCT scans for various diseases such as glaucoma. However, the curr…
This technical note presents a new approach to carrying out the kind of exploration achieved by Thompson sampling, but without explicitly maintaining or sampling from posterior distributions. The approach is based on a bootstrap technique that uses a combination of observed and artificially generated data. The latter serves to induce a prior distribution which, as we will demonstrate, is critical to effective exploration. We explain how the approach can be applied to multi-armed bandit and reinforcement learning problems and how it relates to Thompson sampling. The approach is particularly wel…
DiabetesNet: A Deep Learning Approach to Diabetes Diagnosis
Australian National University（澳大利亚国立大学）；Commonwealth Scientific and Industrial Research Organisation（英联邦科学与工业研究组织）；Curtin University（科廷大学）；Obuda University（欧布达大学）
This is a brief technical note to clarify some of the issues with applying the application of the algorithm posterior sampling for reinforcement learning (PSRL) in environments without fixed episodes. In particular, this paper aims to: - Review some of results which have been proven for finite horizon MDPs (Osband et al 2013, 2014a, 2014b, 2016) and also for MDPs with finite ergodic structure (Gopalan et al 2014). - Review similar results for optimistic algorithms in infinite horizon problems (Jaksch et al 2010, Bartlett and Tewari 2009, Abbasi-Yadkori and Szepesvari 2011), with particular att…
Amirkabir University of Technology（阿米尔卡比尔理工大学）；University of Zanjan（赞詹大学）
This paper proposes a new RWO-Sampling (Random Walk Over-Sampling) based on graphs for imbalanced datasets. In this method, two schemes based on under-sampling and over-sampling methods are introduced to keep the proximity information robust to noises and outliers. After constructing the first graph on minority class, RWO-Sampling will be implemented on selected samples, and the rest will remain unchanged. The second graph is constructed for the majority class, and the samples in a low-density area (outliers) are removed. Finally, in the proposed method, samples of the majority class in a high…
Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty
Faculty of Engineering and Natural Sciences（工程与自然科学学院）；Department of Information and Communications Engineering（信息与通信工程系）；Department of Management, Economics and Industrial Engineering（管理、经济与工业工程系）
Anomaly detection is critical in various fields, including intrusion detection, health monitoring, fault diagnosis, and sensor network event detection. The isolation forest (or iForest) approach is a well-known technique for detecting anomalies. It is, however, ineffective when dealing with dynamic streaming data, which is becoming increasingly prevalent in a wide variety of application areas these days. In this work, we extend our previous work by proposed an efficient iForest based approach for anomaly detection using cube sampling that is effective on streaming data. Cube sampling is used i…
Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network
Vasyl Stefanyk Precarpathian National University（瓦西里·斯特凡尼克外喀尔巴阡国立大学）；Ivano-Frankivsk National Technical University of Oil and Gas（伊万诺-弗兰科夫斯克国立石油与天然气技术大学）
School of Mathematics（数学学院）；University of Edinburgh（爱丁堡大学）
Minibatching is a very well studied and highly popular technique in supervised learning, used by practitioners due to its ability to accelerate training through better utilization of parallel processing power and reduction of stochastic variance. Another popular technique is importance sampling -- a strategy for preferential sampling of more important examples also capable of accelerating the training process. However, despite considerable effort by the community in these areas, and due to the inherent technical difficulty of the problem, there is no existing work combining the power of import…
We explore several oversampling techniques for an imbalanced multi-label classification problem, a setting often encountered when developing models for Computer-Aided Diagnosis (CADx) systems. While most CADx systems aim to optimize classifiers for overall accuracy without considering the relative distribution of each class, we look into using synthetic sampling to increase per-class performance when predicting the degree of malignancy. Using low-level image features and a random forest classifier, we show that using synthetic oversampling techniques increases the sensitivity of the minority c…
UniFault: A Fault Diagnosis Foundation Model from Bearing Data
Department of Computer Science, Khalifa University（卡利法大学计算机科学系）；Institute for Infocomm Research, A*STAR（信息与通信研究所，A*STAR）；Centre for Frontier AI Research, A*STAR（前沿人工智能研究中心，A*STAR）；Propulsion and Space Research Center, Technology Innovation Institute（推进与空间研究中心，技术创新研究所）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Center for Industrial Artificial Intelligence, Department of Mechanical Engineering, A. James Clark School of Engineering, University of Maryland（工业人工智能中心，机械工程系，A. James Clark工程学院，马里兰大学）
Thoracic diseases are very serious health problems that plague a large number of people. Chest X-ray is currently one of the most popular methods to diagnose thoracic diseases, playing an important role in the healthcare workflow. However, reading the chest X-ray images and giving an accurate diagnosis remain challenging tasks for expert radiologists. With the success of deep learning in computer vision, a growing number of deep neural network architectures were applied to chest X-ray image classification. However, most of the previous deep neural network classifiers were based on deterministi…
