6 citations · 11 across the 14 of their papers we have counts for
14 papers
Attention-Based Estimation of the Individual Treatment Benefit Probability under Dose Variation
Lev V. Utkin, Andrei V. Konstantinov, Stanislav K. Kogan +2
Estimating the probability that a treatment outperforms a control for an individual patient, called the Individual Probability of Treatment Benefit (IPTB), offers a clinically intu…
SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis
Stanislav R. Kirpichenko, Andrei V. Konstantinov, Lev V. Utkin
Survival analysis aims to estimate a time-to-event distribution from data with censored observations. Many existing methods either impose structural assumptions on the hazard funct…
Towards a Relationship-Aware Transformer for Tabular Data
Andrei V. Konstantinov, Valerii A. Zuev, Lev V. Utkin
Deep learning models for tabular data typically do not allow for imposing a graph of external dependencies between samples, which can be useful for accounting for relatedness in ta…
Survival Analysis as Imprecise Classification with Trainable Kernels
Andrei V. Konstantinov, Vlada A. Efremenko, Lev V. Utkin
Survival analysis is a fundamental tool for modeling time-to-event data in healthcare, engineering, and finance, where censored observations pose significant challenges. While trad…
Ensemble-Based Survival Models with the Self-Attended Beran Estimator Predictions
Lev V. Utkin, Semen P. Khomets, Vlada A. Efremenko +2
Survival analysis predicts the time until an event of interest, such as failure or death, but faces challenges due to censored data, where some events remain unobserved. Ensemble-b…
Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges
Valerii A. Zuev, Elena G. Salmagambetova, Stepan N. Djakov +1
Epilepsy is typically diagnosed through electroencephalography (EEG) and long-term video-EEG (vEEG) monitoring. The manual analysis of vEEG recordings is time-consuming, necessitat…
A novel gradient-based method for decision trees optimizing arbitrary differential loss functions
Andrei V. Konstantinov, Lev V. Utkin
There are many approaches for training decision trees. This work introduces a novel gradient-based method for constructing decision trees that optimize arbitrary differentiable los…
Survival Concept-Based Learning Models
Stanislav R. Kirpichenko, Lev V. Utkin, Andrei V. Konstantinov +1
Concept-based learning enhances prediction accuracy and interpretability by leveraging high-level, human-understandable concepts. However, existing CBL frameworks do not address su…
SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and Several Attention Mechanisms
Lev V. Utkin, Semen P. Khomets, Vlada A. Efremenko +1
Many ensemble-based models have been proposed to solve machine learning problems in the survival analysis framework, including random survival forests, the gradient boosting machin…
FI-CBL: A Probabilistic Method for Concept-Based Learning with Expert Rules
Lev V. Utkin, Andrei V. Konstantinov, Stanislav R. Kirpichenko
A method for solving concept-based learning (CBL) problem is proposed. The main idea behind the method is to divide each concept-annotated image into patches, to transform the patc…
Concept-based Explainable Malignancy Scoring on Pulmonary Nodules in CT Images
Rinat I. Dumaev, Sergei A. Molodyakov, Lev V. Utkin
To increase the transparency of modern computer-aided diagnosis (CAD) systems for assessing the malignancy of lung nodules, an interpretable model based on applying the generalized…
Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning
Andrei V. Konstantinov, Lev V. Utkin
A problem of incorporating the expert rules into machine learning models for extending the concept-based learning is formulated in the paper. It is proposed how to combine logical…
Generating Survival Interpretable Trajectories and Data
Andrei V. Konstantinov, Stanislav R. Kirpichenko, Lev V. Utkin
A new model for generating survival trajectories and data based on applying an autoencoder of a specific structure is proposed. It solves three tasks. First, it provides prediction…
Dual feature-based and example-based explanation methods
Andrei V. Konstantinov, Boris V. Kozlov, Stanislav R. Kirpichenko +1
A new approach to the local and global explanation is proposed. It is based on selecting a convex hull constructed for the finite number of points around an explained instance. The…