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arXiv 2025年

作者： Jürgens, Mira Mortier, Thomas Hüllermeier, Eyke Bengs, Viktor Waegeman, Willem Department of Data Analysis and Mathematical Modeling Ghent University Belgium Department of Environment Ghent University Belgium Department of Informatics Munich Center for Machine Learning LMU Munich Germany

The accurate representation of epistemic uncertainty is a challenging yet essential task in machine learning. A widely used representation corresponds to convex sets of probabilistic predictors, also known as credal sets. One popular way of constructing these credal sets is via ensembling or specialized supervised learning methods, where the epistemic uncertainty can be quantified through measures such as the set size or the disagreement among members. In principle, these sets should contain the true data-generating distribution. As a necessary condition for this validity, we adopt the strongest notion of calibration as a proxy. Concretely, we propose a novel statistical test to determine whether there is a convex combination of the set’s predictions that is calibrated in distribution. In contrast to previous methods, our framework allows the convex combination to be instance dependent, recognizing that different ensemble members may be better calibrated in different regions of the input space. Moreover, we learn this combination via proper scoring rules, which inherently optimize for calibration. Building on differentiable, kernel-based estimators of calibration errors, we introduce a nonparametric testing procedure and demonstrate the benefits of capturing instance-level variability on of synthetic and real-world experiments. © 2025, CC BY.

关键词： Self-supervised learning

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Conformal Prediction in Hierarchical Classification

arXiv

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arXiv 2025年

作者： Mortier, Thomas Javanmardi, Alireza Sale, Yusuf Hüllermeier, Eyke Waegeman, Willem Department of Environment Ghent University Ghent Belgium Department of Data Analysis and Mathematical Modelling Ghent University Ghent Belgium Institute of Informatics LMU Munich Munich Germany Munich Center for Machine Learning Munich Germany

Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In this work, we extend the split conformal prediction framework to hierarchical classification, where prediction sets are commonly restricted to internal nodes of a predefined hierarchy, and propose two computationally efficient inference algorithms. The first algorithm returns internal nodes as prediction sets, while the second relaxes this restriction, using the notion of representation complexity, yielding a more general and combinatorial inference problem, but smaller set sizes. Empirical evaluations on several benchmark datasets demonstrate the effectiveness of the proposed algorithms in achieving nominal coverage. © 2025, CC BY.

关键词： Prediction models

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Multi-Stage Bidirectional Cross-Attention Model for Predicting Prognosis in Multiple Peritoneum Lesions with Clinical Information

Multi-Stage Bidirectional Cross-Attention Model for Predicti...

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IEEE International Symposium on Biomedical Imaging

作者： Haoshen Li Jieyuan Cai Yiyuan Wei Zifan Chen Heyun Chen Jie Zhao Yanjie Shi Bin Dong Lei Tang Xiaotian Zhang Li Zhang Center for Data Science Peking University China Peking University Cancer Hospital & Institute China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Peritoneal metastasis occurs when cancer cells spread from the primary tumor to the peritoneum, leading to morphological alterations that significantly impact patient survival. The specific changes across multiple peritoneal sites can effectively indicate a patient's risk level and prognosis. However, the considerable variation in peritoneal shape, size, and location poses significant challenges for feature extraction and prognostic analysis. Traditional multi-instance learning approaches typically fuse instance features extracted by the backbone network at the final stage, but their lack of intermediate interaction limits the ability to capture both common and distinct features. To address this limitation, we propose a Multi-Stage BiDirectional Cross-Attention framework (MSBDCA). Our approach enhances feature extraction and prognostic analysis by facilitating interaction among different peritoneal instances and incorporating clinical information. Experimental comparisons with five baseline models demonstrate our method's superiority, achieving improvements of 8.7% in Area Under Curve (AUC) and 5.9% in Concordance Index (C_index). These results suggest a promising direction for survival and prognostic analysis using peritoneal lesions. Our code is available at https://***/HaoshenLi/MSBDCA.

关键词： learning systems Shape Predictive models Feature extraction Metastasis Lesions Clinical diagnosis Prognostics and health management Abdomen Biomedical imaging

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U-Star: AN Asymmetric U-Shaped Network Based on Element-Wise Multiplication to Segment Nuclei in H&E Stained Histological Images

U-Star: AN Asymmetric U-Shaped Network Based on Element-Wise...

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IEEE International Symposium on Biomedical Imaging

作者： Guangzhengao Yang Li Zhang Jie Zhao Zifan Chen Haoshen Li Bin Dong Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Nuclei segmentation in Hematoxylin and Eosin (H&E) stained images plays a crucial role in cancer diagnosis and pathological evaluation, enabling pathologists to identify abnormal cells and assess their morphology and distribution. While current automated nuclei segmentation methods predominantly employ convolutional neural networks and attention mechanisms, the potential of element-wise multiplication has been largely unexplored. This paper introduces U-Star, a novel asymmetric segmentation network based on the star block that leverages element-wise multiplication. U-Star adopts the classic encoder-decoder architecture and innovatively implements star-connection as an alternative to traditional skip-connections. In experiments on an H&E stained image dataset, U-Star achieved superior performance with a Dice coefficient of 0.8783, accuracy of 0.9089, and IoU of 0.7929, significantly outperforming baseline models. Extensive ablation studies validate the effectiveness of the star-connection and demonstrate the advantages of our proposed framework. Beyond advancing the application of element-wise multiplication techniques, U-Star shows promising potential for broader applications in medical image segmentation.

关键词： Image segmentation Pathology Technological innovation Image analysis Stars Morphology Network architecture Medical diagnostic imaging Tuning Cancer

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RVPD: An Automated System for Calculating the Tortuosity and Bifurcation Angles of Retinal Vessels to Predict Diseases

RVPD: An Automated System for Calculating the Tortuosity and...

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IEEE International Symposium on Biomedical Imaging

作者： Guangzhengao Yang Xingyu Luo Jie Zhao Fangfang Fan Haoshen Li Bin Dong Li Zhang Yan Zhang Center for Data Science Peking University China Department of Cardiology Peking University First Hospital China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Hypertension and diabetes are known to potentially cause morphological changes in the retinal capillary system, yet quantifying these changes presents significant challenges. This research addresses this issue by designing and developing an automated system capable of analyzing retinal images to detect and measure these changes. Our system automatically segments vessels and the optic disc, calculates tortuosity and bifurcation angles, and performs disease prediction based on these measurements along with clinical features. For tortuosity calculation, we adopted a novel curve fitting method, which provides more stable convergence compared to traditional discrete numerical methods. The system integrates clinical data and conducts prediction experiments on hypertensive and diabetic patients with varying sample sizes. Our results demonstrate the system's strong performance and scalability: in the hypertension group, the AUC reached a maximum of 0.814 ± 0.023, with an ACC of 0.735 ± 0.033, while in the diabetes group, the AUC reached a maximum of 0.710 ± 0.043, with an ACC of 0.641 ± 0.052. These findings suggest that our automated system could serve as a valuable tool for early detection and risk assessment of hypertension and diabetes, potentially improving patient outcomes through timely intervention.

关键词： Hypertension Image segmentation Scalability Bifurcation Optical variables measurement Optical imaging Retinal vessels Diabetes Risk management Diseases

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Harmonizing Social and Production Indicators in the Way to Technological Sovereignty 15

Harmonizing Social and Production Indicators in the Way to T...

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Goroshko, Igor V. Smirnov, Mikhail V. Financial University Data Analysis and Machine Learning Department Moscow Russia

Achieving national technological sovereignty is a complex problem that combines the tasks of social and industrial development. The article contains the results of a study on elaborating mechanisms for harmonizing the... 详细信息

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

关键词： Economic and social effects

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The method of residual‑based bootstrap averaging of the forecast ensemble

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Financial Innovation 2023年第1期9卷 991-1002页

作者： Vera Ivanyuk Department of Data Analysis and Machine Learning Financial University49 Leningradsky ProspektMoscowRussia 125993

This paper presents an optimization approach—residual-based bootstrap averaging(RBBA)—for different types of forecast *** traditional residual-mean-square-error-based ensemble forecast averaging approaches,the RBBA method attempts to find optimal forecast weights in an ensemble and allows for their combi-nation into the most effective additive *** the RBBA method,all the different types of forecasts obtain the optimal weights for ensemble residuals that are statisti-cally optimal in terms of the fitness function of the *** studies have been conducted to demonstrate why and how the RBBA method *** experi-mental results based on the real-world time series of contemporary stock exchanges show that the RBBA method can produce ensemble forecasts with good generalization ability.

关键词： Forecast ensembles Time series Artificial neural networks

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Application of factor analysis and neural networks for prediction in taxi service 14

Application of factor analysis and neural networks for predi...

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14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Andriyanov, Nikita Financial University Government of the Russian Federation Data Analysis and Machine Learning Department Moscow Russia

ISBN: (纸本)9781665412308

In this work, a study of multidimensional data on the operation of a taxi ordering service was carried out in order to predict the cost of an application. It is shown that it is possible to single out the main components, on the basis of which a sufficiently accurate prediction with respect to the initial data set is possible. © 2021 IEEE.

关键词： Forecasting

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Methodology for Training data Science Skills Based on Competitions on the Kaggle Platform

Methodology for Training Data Science Skills Based on Compet...

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2023 Seminar on Information Computing and Processing, ICP 2023

作者： Kalazhokov, Zaur Kh. Makoveichuk, Yan T. Financial University under the Government of the Russian Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350369830

The article describes the experience of teaching students of Big data analytics-related fields the basics of data science. The teaching methodology used at the Financial University under the Government of the Russian Federation is given, which consists in involvement of students of the group to competitions on Google Kaggle platform. In this methodology, the positive factors influencing the rapid acquisition of initial data science skills are: competitive effect;availability of prepared datasets and problem formulations from the real business sector in various directions;possibility of instant verification of the found solution;practical unlimited number of solutions. These factors contribute to the rapid understanding by students of the need to understand the essence of the problem for the best results, obligatory familiarization with the data, data cleaning and formatting, exploratory data analysis, forming a model with some basic level and its improvement, as well as interpretation. © 2023 IEEE.

关键词： Decision trees

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IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors

arXiv

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arXiv 2025年

作者： Dia, Noé Yantovski-Barth, M.J. Adam, Alexandre Bowles, Micah Perreault-Levasseur, Laurence Hezaveh, Yashar Scaife, Anna Université de Montréal Montréal Canada Mila - Quebec Artificial Intelligence Institute Montréal Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Department of Astrophysics University of Oxford Oxford United Kingdom Flatiron Institute Center for Computational Astrophysics New York United States Perimeter Institute Waterloo Canada Trottier Space Institute McGill University Montréal Canada University of Manchester Manchester United Kingdom The Alan Turing Institute United Kingdom

Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challenge in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples. Our code is open source and freely available at https://***/EnceladeCandy/IRIS. © 2025, CC BY.

关键词： Gaussian distribution

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