6 citations · 11 across the 14 of their papers we have counts for
6 papers · 1 filter
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…