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arXiv 2025年

作者： Abdelmalak, Ibram Madhusudhanan, Kiran Choi, Jungmin Stubbemann, Maximilian Schmidt-Thieme, Lars Information Science and Machine Learning Lab VWFS Data Analytics Research Center University of Hildesheim Niedersachsen Hildesheim Germany

Time-series forecasting research has converged to a small set of datasets and a standardized collection of evaluation scenarios. Such a standardization is to a specific extent needed for comparable research. However, the underlying assumption is, that the considered setting is a representative for the problem as a whole. In this paper, we challenge this assumption and show that the current scenario gives a strongly biased perspective on the state of time-series forecasting research. To be more detailed, we show that the current evaluation scenario is heavily biased by the simplicity of the current datasets. We furthermore emphasize, that when the lookback-window is properly tuned, current models usually do not need any information flow across channels. However, when using more complex benchmark data, the situation changes: Here, modeling channel-interactions in a sophisticated manner indeed enhances performances. Furthermore, in this complex evaluation scenario, Crossformer, a method regularly neglected as an important baseline, is the SOTA method for time series forecasting. Based on this, we present the Fast Channel-dependent Transformer (FaCT), a simplified version of Crossformer which closes the runtime gap between Crossformer and TimeMixer, leading to an efficient model for complex forecasting datasets. © 2025, CC BY.

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An AI system for continuous knee osteoarthritis severity grading: An anomaly detection inspired approach with few labels

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Artificial Intelligence in Medicine 2025年 167卷

作者： Niamh Belton Aonghus Lawlor Kathleen M. Curran Science Foundation Ireland Centre for Research Training in Machine Learning Ireland School of Medicine University College Dublin Ireland School of Computer Science University College Dublin Ireland Insight Centre for Data Analytics University College Dublin Dublin Ireland

The diagnostic accuracy and subjectivity of existing Knee Osteoarthritis (OA) ordinal grading systems has been a subject of on-going debate and concern. Existing automated solutions are trained to emulate these imperfect systems, whilst also being reliant on large annotated databases for fully-supervised training. This work proposes a three stage approach for automated continuous grading of knee OA that is built upon the principles of Anomaly Detection (AD); learning a robust representation of healthy knee X-rays and grading disease severity based on its distance to the centre of normality. In the first stage, SS-FewSOME is proposed, a self-supervised AD technique that learns the ‘normal’ representation, requiring only examples of healthy subjects and < 3 % of the labels that existing methods require. In the second stage, this model is used to pseudo label a subset of unlabelled data as ‘normal’ or ‘anomalous’, followed by denoising of pseudo labels with CLIP. The final stage involves retraining on labelled and pseudo labelled data using the proposed Dual Centre Representation learning (DCRL) which learns the centres of two representation spaces; normal and anomalous. Disease severity is then graded based on the distance to the learned centres. The proposed methodology outperforms existing techniques by margins of up to 24% in terms of OA detection and the disease severity scores correlate with the Kellgren-Lawrence grading system at the same level as human expert performance. Code available at https://***/niamhbelton/SS-FewSOME_Disease_Severity_Knee_Osteoarthritis .

关键词： Few shot anomaly detection Knee osteoarthritis Self-supervision Self-supervised learning X-ray Contrastive learning Few labels CLIP Deep learning machine learning Artificial intelligence

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ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing

ProbSAINT: Probabilistic Tabular Regression for Used Car Pri...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Madhusudhanan, Kiran Behrens, Gunnar Stubbemann, Maximilian Schmidt-Thieme, Lars University Of Hildesheim Information Systems and Machine Learning Lab Vwfs Data Analytics Research Center Germany Volkswagen Financial Services Ag Data Analytics & Ai Engineering United States

ISBN: (纸本)9798350362480

Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increased demand for used cars, accurate pricing would benefit both buyers and sellers by ensuring fair transactions. However, the transition towards automated pricing algorithms using machine learning necessitates the comprehension of model uncertainties, specifically the ability to flag predictions that the model is unsure about. Although recent literature proposes the use of boosting algorithms or nearest neighbor-based approaches for swift and precise price predictions, encapsulating model uncertainties with such algorithms presents a complex challenge. We introduce ProbSAINT, a model that offers a principled approach for uncertainty quantification of its price predictions, along with accurate point predictions that are comparable to state-of-the-art boosting techniques. Furthermore, acknowledging that the business prefers pricing used cars based on the number of days the vehicle was listed for sale, we show how ProbSAINT can be used as a dynamic forecasting model for predicting price probabilities for different expected offer durations. Our experiments further indicate that ProbSAINT is especially accurate in instances where it is highly certain. This proves the applicability of its probabilistic predictions in real-world scenarios where trustworthiness is crucial. © 2024 IEEE.

关键词： Automotive industry

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Bias Bubbles: Using Semi-Supervised learning to Measure How Many Biased News Articles Are Around Us 29

Bias Bubbles: Using Semi-Supervised Learning to Measure How ...

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29th Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2021

作者： Ruan, Qin Namee, Brian Mac Dong, Ruihai School of Computer Science University College Dublin Dublin Ireland Insight Centre for Data Analytics Science Foundation Ireland Centre for Research Training in Machine Learning Ireland

The proliferation of web 2.0 technology allows us to easily create and share online content, but also leads to the rapid spread of misinformation and biased media, which has considerable negative effects on society. Deep learning-based classifiers are one common way of identifying media bias, but they suffer from a lack of large-scale labelled datasets. In this paper, we first explore the use of pseudo-labelling technology to mitigate this problem. Second, we exploit a masking method to identify biased sentences in news articles by iteratively masking each sentence from an article and observing the change in output of a bias detection model. These identified sentences not only contribute to evaluating the proposed model, but also enable end-users to understand where media bias arises in an article. Finally, we apply our well-trained bias detection model to a well-known news article dataset to show how widespread media bias is-the results show that it is rampant and has become a serious social problem that we cannot ignore. © 2021 CEUR-WS. All rights reserved.

关键词： Deep learning

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Finding latent causes in causal networks: An efficient approach based on Markov blankets

Finding latent causes in causal networks: An efficient appro...

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22nd Annual Conference on Neural Information Processing Systems, NIPS 2008

作者： Pellet, Jean-Philippe Elisseeff, André Pattern Recognition and Machine Learning Group Swiss Federal Institute of Technology Zurich 8092 Zurich Switzerland Data Analytics Group IBM Research GmbH 8803 Ruschlikon Switzerland

ISBN: (纸本)9781605609492

Causal structure-discovery techniques usually assume that all causes of more than one variable are observed. This is the so-called causal sufficiency assumption. In practice, it is untestable, and often violated. In this paper, we present an efficient causal structure-learning algorithm, suited for causally insufficient data. Similar to algorithms such as IC* and FCI, the proposed approach drops the causal sufficiency assumption and learns a structure that indicates (potential) latent causes for pairs of observed variables. Assuming a constant local density of the data-generating graph, our algorithm makes a quadratic number of conditional-independence tests w.r.t. the number of variables. We show with experiments that our algorithm is comparable to the state-of-the-art FCI algorithm in accuracy, while being several orders of magnitude faster on large problems. We conclude that MBCS* makes a new range of causally insufficient problems computationally tractable.

关键词： learning algorithms

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System Architecture for Reading and Interpreting Physical Printouts of Medical Forms 22

System Architecture for Reading and Interpreting Physical Pr...

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22nd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2021

作者： Snegireva, Ekaterina Khazankin, Grigory R. Mikheenko, Igor Stream Data Analytics and Machine Learning Laboratory Novosibirsk State University Novosibirsk Russia Novosibirsk State University Novosibirsk Russia Meshalkin National Medical Research Center Novosibirsk Russia

ISBN: (纸本)9781665414982

This article describes the developed architecture of the system module for processing and interpreting analog medical data. Patients often undergo examinations in various medical institutions, and since their results are often handed out to the patient in printed form, the receiving institution transfers them to its database manually. There is also a tendency to completely refuse analog media and use only digital ones. But in this case, another problem appears-either loss or conversion of the accumulated analog base into digital format. These days, automatic document management systems for medical institutions-Health information systems (HIS)-are actively developing. The software module developed in accordance with the architecture described in the article can be used by developers of various HIS to automate the work with analog data. If it is necessary, it can also be freely expanded by adding new modules for working with various analog data. In this article, we take ECG scans and medical test results as examples of such data. As a result of the work undertaken the prototype of the designed system was developed and tested. © 2021 IEEE.

关键词： Medical information systems

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XAIMed-Net: Towards Explainable Brain Tumour Detection in 2D T1-weighted CE-MRI Images Using Transfer learning 26

XAIMed-Net: Towards Explainable Brain Tumour Detection in 2D...

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26th Irish machine Vision and Image Processing Conference, IMVIP 2024

作者： Adegboro, Oluwabukola Ganepola, Vayangi Dietlmeier, Julia Mazo, Claudia O'Connor, Noel E. SFI Centre for Research Training in Machine Learning Dublin City University Ireland Insight SFI Research Centre for Data Analytics Dublin City University Ireland School of Computing Dublin City University Ireland

ISBN: (纸本)9781837242672

Malignant brain tumours comprise one of the deadliest types of cancer and their timely and accurate detection is receiving urgent attention. Radiology can benefit from the recent impressive progress of AI and deep learning that provide the non-invasive diagnostic opportunity. However, the challenge of explainable AI-based decision-making processes remains. In this work, we aim to take a step toward more explainable classification of brain tumours in medical images by designing a new convolutional architecture while also leveraging the advantages of transfer learning. Specifically, we introduce a new shallow convolutional head that can be added to a pre-trained ResNet50 model to produce EigenCAM-based importance heatmaps. We investigate pre-training based on the ImageNet and RadImageNet datasets. Furthermore, we also address the problem of weakly-supervised brain tumour segmentation in a challenging 2D T1-weighted CE-MRI dataset by developing an approach to post-process the resulting heatmaps. Our preliminary experimental results with the Dice Score Coefficient of 0.145 and the new objective distance metric μΔ = 59.55 show the potential of combining large pre-trained models with shallow convolutional architectures to localize tumours using only weak classification labels. Our code is publicly available at https://***/juliadietlmeier/Explainable_brain_tumor_detection_in_MRI. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Transfer learning

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XAI-ResUNet: Analysing the Impact of Pre-training in ResUNet Architectures for Multiple Sclerosis Lesion Segmentation using EigenGradCAM 26

XAI-ResUNet: Analysing the Impact of Pre-training in ResUNet...

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26th Irish machine Vision and Image Processing Conference, IMVIP 2024

作者： Ganepola, Vayangi Mathur, Prateek Adegboro, Oluwabukola Dietlmeier, Julia Lawlor, Aonghus O'Connor, Noel E. Mazo, Claudia SFI Centre for Research Training in Machine Learning Dublin Ireland Insight SFI Research Centre for Data Analytics Dublin Ireland Dublin City University Dublin Ireland University College Dublin Dublin Ireland

ISBN: (纸本)9781837242672

Multiple Sclerosis (MS) is a chronic disease that causes lesions in the central nervous system. Diagnosing MS is a challenging process that strongly relies on Magnetic Resonance Imaging (MRI) and exhibits high and largely unexplained variability across patients. In this paper, we propose a novel XAI-ResUNet architecture by integrating ResNet-50 as an encoder in a U-Net-like architecture and demystifying the detection and segmentation process by incorporating EigenGradCAM into the encoder. We used the training and testing sets of the MSSEG-2016 dataset for our experiments. We compared three architectures with ResNet-50 pre-trained on ImageNet, RadImageNet, and without pre-training. Our preliminary results show that ResNet-50 pre-trained on ImageNet outperforms the others, achieving a DSC of 0.638. We also demonstrate how pre-training on large image datasets can affect MS lesion detection and segmentation. Our code is publicly available at https://***/VishmiVishara/XAI-ResUNet. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Magnetic resonance imaging

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ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing

ProbSAINT: Probabilistic Tabular Regression for Used Car Pri...

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IEEE International Conference on Big data

作者： Kiran Madhusudhanan Gunnar Behrens Maximilian Stubbemann Lars Schmidt-Thieme Information Systems and Machine Learning Lab VWFS Data Analytics Research Center University Of Hildesheim Data Analytics & AI Engineering Volkswagen Financial Services AG

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

关键词： Industries Accuracy Uncertainty machine learning algorithms Pricing Predictive models Probabilistic logic Prediction algorithms Automobiles Vehicle dynamics

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Hyperparameter Tuning MLP's for Probabilistic Time Series Forecasting

arXiv

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arXiv 2024年

作者： Madhusudhanan, Kiran Jawed, Shayan Schmidt-Thieme, Lars Information Systems and Machine Learning Lab VWFS Data Analytics Research Center University of Hildesheim Hildesheim Germany

Time series forecasting attempts to predict future events by analyzing past trends and patterns. Although well researched, certain critical aspects pertaining to the use of deep learning in time series forecasting remain ambiguous. Our research primarily focuses on examining the impact of specific hyperparameters related to time series, such as context length and validation strategy, on the performance of the state-of-the-art MLP model in time series forecasting. We have conducted a comprehensive series of experiments involving 4800 configurations per dataset across 20 time series forecasting datasets, and our findings demonstrate the importance of tuning these parameters. Furthermore, in this work, we introduce the largest metadataset for time series forecasting to date, named TSBench, comprising 97200 evaluations, which is a twentyfold increase compared to previous works in the field. Finally, we demonstrate the utility of the created metadataset on multi-fidelity hyperparameter optimization tasks. © 2024, CC BY.

关键词： Forecasting

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