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arXiv 2025年

作者： Kant, Wouter M.R. Krijthe, Jesse H. Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands Pattern recognition & Bioinformatics Group EEMCS Delft University of Technology Delft Netherlands

To estimate the causal effect of treatments that vary over time from observational data, one must adjust for time-varying confounding. A common procedure to address confounding is the use of inverse probability of treatment weighting methods. However, the timing of covariate measurements is often irregular, which may introduce additional confounding bias as well as selection bias into the causal effect estimate. Two reweighting methods have been proposed to adjust for these biases: time-as-confounder and reweighting by measurement time. However, it is currently not well understood in which situations these irregularly timed measurements induce bias, and how the available reweighting methods compare to each other in different situations. In this work, we provide a complete inventarization of all possible backdoor paths through which bias is induced. Based on these paths, we distinguish three categories of confounding bias by measurement time: direct confounding (DC), confounding through measured variables (CMV), and confounding through unmeasured variables (CUV). These categories differ in the assumptions and reweighting methods necessary to adjust for bias and may occur simultaneously with selection bias. Through simulation studies, we illustrate: 1. Reweighting by measurement time may be used to adjust for selection bias and confounding through measured variables;2. Time-as-confounder may be used to adjust for all categories of confounding bias, but not selection bias;3. In some cases, the use of a combination of both techniques may be used to adjust for both confounding and selection bias. We finally apply the categorization and reweighting methods on the pre-DIVA1 data set. Adjusting for measurement times is crucial in order to avoid bias, and the categorization of biases and techniques that we introduce may help researchers to choose the appropriate analysis. Copyright © 2025, The Authors. All rights reserved.

关键词： Inverse problems

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Barnes-Hut-SNE 1

Barnes-Hut-SNE

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1st International Conference on Learning Representations, ICLR 2013

作者： van der Maaten, Laurens Pattern Recognition and Bioinformatics Group Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

The paper presents an O(N log N)-implementation of t-SNE — an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots and that normally runs in O(N2). The new implementation uses vantage-point trees to compute sparse pairwise similarities between the input data objects, and it uses a variant of the Barnes-Hut algorithm to approximate the forces between the corresponding points in the embedding. Our experiments show that the new algorithm, called Barnes-Hut-SNE, leads to substantial computational advantages over standard t-SNE, and that it makes it possible to learn embeddings of data sets with millions of objects. © 2013 International Conference on Learning Representations, ICLR. All rights reserved.

关键词： Embeddings

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Speeding Up Tracking by Ignoring Features

Speeding Up Tracking by Ignoring Features

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IEEE Conference on Computer Vision and pattern recognition

作者： Lu Zhang Hamdi Dibeklioglu Laurens van der Maaten Pattern Recognition & Bioinformatics Group Delft University of Technology

ISBN: (纸本)9781479951192

Most modern object trackers combine a motion prior with sliding-window detection, using binary classifiers that predict the presence of the target object based on histogram features. Although the accuracy of such trackers is generally very good, they are often impractical because of their high computational requirements. To resolve this problem, the paper presents a new approach that limits the computational costs of trackers by ignoring features in image regions that - after inspecting a few features - are unlikely to contain the target object. To this end, we derive an upper bound on the probability that a location is most likely to contain the target object, and we ignore (features in) locations for which this upper bound is small. We demonstrate the effectiveness of our new approach in experiments with model-free and model-based trackers that use linear models in combination with HOG features. The results of our experiments demonstrate that our approach allows us to reduce the average number of inspected features by up to 90% without affecting the accuracy of the tracker.

关键词： Target tracking Feature extraction Computational modeling Videos Upper bound Accuracy

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Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification

arXiv

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arXiv 2023年

作者： Ghorbani, Ramin Reinders, Marcel J.T. Tax, David M.J. Pattern Recognition and Bioinformatics group Delft University of Technology Delft Netherlands

Photoplethysmography (PPG) signals, typically acquired from wearable devices, hold significant potential for continuous fitness-health monitoring. In particular, heart conditions that manifest in rare and subtle deviating heart patterns may be interesting. However, robust and reliable anomaly detection within these data remains a challenge due to the scarcity of labeled data and high inter-subject variability. This paper introduces a two-stage framework leveraging representation learning and personalization to improve anomaly detection performance in PPG data. The proposed framework first employs representation learning to transform the original PPG signals into a more discriminative and compact representation. We then apply three different unsupervised anomaly detection methods for movement detection and biometric identification. We validate our approach using two different datasets in both generalized and personalized scenarios. The results show that representation learning significantly improves anomaly detection performance while reducing the high inter-subject variability. Personalized models further enhance anomaly detection performance, underscoring the role of personalization in PPG-based fitness-health monitoring systems. The results from biometric identification show that it’s easier to distinguish a new user from one intended authorized user than from a group of users. Overall, this study provides evidence of the effectiveness of representation learning and personalization for anomaly detection in PPG data. © 2023, CC BY-NC-ND.

关键词： Anomaly detection

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Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability

arXiv

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arXiv 2022年

作者： Ghorbani, Ramin Reinders, Marcel T.J. Tax, David M.J. Pattern Recognition and Bioinformatics group Delft University of Technology Delft Netherlands

With the progress of sensor technology in wearables, the collection and analysis of PPG signals are gaining more interest. Using Machine Learning, the cardiac rhythm corresponding to PPG signals can be used to predict different tasks such as activity recognition, sleep stage detection, or more general health status. However, supervised learning is often limited by the amount of available labeled data, which is typically expensive to obtain. To address this problem, we propose a Self-Supervised Learning (SSL) method with a pretext task of signal reconstruction to learn an informative generalized PPG representation. The performance of the proposed SSL framework is compared with two fully supervised baselines. The results show that in a very limited label data setting (10 samples per class or less), using SSL is beneficial, and a simple classifier trained on SSL-learned representations outperforms fully supervised deep neural networks. However, the results reveal that the SSL-learned representations are too focused on encoding the subjects. Unfortunately, there is high inter-subject variability in the SSL-learned representations, which makes working with this data more challenging when labeled data is scarce. The high inter-subject variability suggests that there is still room for improvements in learning representations. In general, the results suggest that SSL may pave the way for the broader use of machine learning models on PPG data in label-scarce regimes. © 2022, CC BY.

关键词： pattern recognition

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Accelerating Cost Aggregation for Real-Time Stereo Matching

Accelerating Cost Aggregation for Real-Time Stereo Matching

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jianbin Fang Ana Lucia Varbanescu Jie Shen Henk Sips Gorkem Saygili Laurens Van Der Maaten Parallel and Distributed Systems Group Pattern Recognition and Bioinformatics Group Delft University of Technology Delft The Netherlands

Real-time stereo matching, which is important in many applications like self-driving cars and 3-D scene reconstruction, requires large computation capability and high memory bandwidth. The most time-consuming part of stereo-matching algorithms is the aggregation of information (i.e. costs) over local image regions. In this paper, we present a generic representation and suitable implementations for three commonly used cost aggregators on many-core processors. We perform typical optimizations on the kernels, which leads to significant performance improvement (up to two orders of magnitude). Finally, we present a performance model for the three aggregators to predict the aggregation speed for a given pair of input images on a given architecture. Experimental results validate our model with an acceptable error margin (an average of 10.4%). We conclude that GPU-like many-cores are excellent platforms for accelerating stereo matching.

关键词： Kernel Optimization Memory management Acceleration Venus Graphics processing units Image color analysis

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Graph-Based Kinship recognition

Graph-Based Kinship Recognition

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International Conference on pattern recognition

作者： Yuanhao Guo Hamdi Dibeklioglu Laurens Van Der Maaten Section Imaging & Bioinformatics Leiden University The Netherlands Pattern Recognition & Bioinformatics Group Delft University of Technology The Netherlands Pattern Recognition & Bioinf. Group Delft Univ. of Technol. Delft Zuid-Holland NL

ISBN: (纸本)9781479952106

Image-based kinship recognition is an important problem in the reconstruction and analysis of social networks. Prior studies on image-based kinship recognition have focused solely on pair wise kinship verification, i.e. on the question of whether or not two people are kin. Such approaches fail to exploit the fact that many real-world photographs contain several family members, for instance, the probability of two people being brothers increases when both people are recognized to have the same father. In this work, we propose a graph-based approach that incorporates facial similarities between all family members in a photograph in order to improve the performance of kinship recognition. In addition, we introduce a database of group photographs with kinship annotations.

关键词： Databases Feature extraction Face recognition Image recognition Accuracy Estimation Prediction algorithms

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C-reactive protein-guided treatment in pneumonia: charting a personalised approach - Authors' reply

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The Lancet. Respiratory medicine 2025年第5期13卷 e25-e26页

作者： Jim M Smit Jesse H Krijthe Gianfranco U Meduri Pierre-François Dequin Harin Karunajeewa Antoni Torres Marcel J T Reinders Henrik Endeman Philip A Van Der Zee Department of Intensive Care Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Pattern Recognition & Bioinformatics group Delft University of Technology Delft Netherlands. Electronic address: j.smit@erasmusmc.nl. Pattern Recognition & Bioinformatics group Delft University of Technology Delft Netherlands. Department of Pharmaceutical Sciences University of Tennessee Health Science Center Memphis TN USA. Medecine intensive reanimation Chru Hôpitaux De Tours Hospital Bretonneau Tours France. Department of Medicine The Western Health Chronic Disease Alliance and the University of Melbourne Melbourne VIC Australia. Department of Pulmonology Hospital Clinic University of Barcelona Barcelona Spain. Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands. Department of Pulmonary Medicine Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2011年 7036 LNBI卷 V-VI页

作者： Loog, Marco Wessels, Lodewyk Reinders, Marcel De Ridder, Dick Delft University of Technology Pattern Recognition and Bioinformatics Group Mekelweg 4 2628 CD Delft Netherlands Netherlands Cancer Institute Bioinformatics and Statistics Plesmanlaan 121 1066 CX Amsterdam Netherlands

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A preliminary study on visual estimation of taste appreciation

A preliminary study on visual estimation of taste appreciati...

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： İdil Esen Zülfikar Hamdi Dibeklioğlu Hazim Kemal Ekenel Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Pattern Recognition & Bioinformatics Group Delft University of Technology Delft Netherlands

From past to present, individuals' appreciation of taste has always been wondered. Moreover, there is an increasing research interest in measuring taste appreciation. Most of the previous work in this area are psychological studies that rely on manual coding of facial actions and/or emotional expressions. Consequently, these studies depend on human observations. We propose a preliminary study for an automatic visual analysis system that estimates taste liking of individuals. Our results show that the proposed system performs with 56.6% accuracy to classify appreciation in terms of liking, neutral, and disliking categories. In order to explore this result in detail, classification of liking level pairs such as disliking-vs-liking, neutral-vs-liking, and neutral-vs-disliking are also evaluated. Our system achieved 72.5% accuracy for distinguishing between dislike and liking, however, classification accuracy for dislike and neutral, and for liking and neutral have been found to be lower. Our results suggest that reliable and fast automatic systems can be developed to estimate taste appreciation, yet classes of liking and neutral state are not easily separable as indicated in previous studies.

关键词： Feature extraction Videos Databases Support vector machines Visualization Estimation Liquids

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