arXiv subjects

Ekaterina Ivanova

Publications and source records attributed to Ekaterina Ivanova.

6 recordsLinked to original sources

arXiv2026

Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports

Background. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinant of prognosis. Established AF risk scores rely on factors (older age, hypertension) nearly ubiquitous among patients with cardiovascular disease (CVD), offering limited stratification in this high-risk group. Most target long-term (5-10 year) rather than medium-term prediction. We developed interpretable ML models predicting AF risk over a 24-month and entire follow-up horizon in CVD patients using routinely collected hospital data. Methods. Single-center retrospective study of electronic health records from the National Research Cardiology Center (Russia) for patients aged >=18 with CVD but without pre-existing AF, hospitalized more than once between January 2012 and May 2019. A custom NLP pipeline transformed unstructured discharge reports into 73 structured features, combining a rule-based parser with transformer-based NER. Using LightAutoML we built a full model (73 features), a simple model (reduced subset), and a linear model for a bedside risk score. Performance was assessed by ROC AUC, compared with CHARGE-AF, C2HEST, MHS, and HAVOC, and interpreted via SHAP. Results. Of 80,576 records from 45,000 patients, 17,562 met inclusion criteria; 1,438 (8.19%) developed AF. The full model reached ROC AUC 0.735 (24-month) and 0.696 (entire follow-up); the simple model was nearly identical (0.725, 0.696). All non-linear models outperformed the four clinical risk scores (ROC AUC 0.53-0.64). The simple model uses 13 features and is named Pre-AF 13. SHAP identified age and left atrial volume as dominant predictors. A linear risk score (Pre-AF 9) stratified observed 24-month AF incidence from ~7% to 36%. Conclusion. Interpretable ML models built from routinely collected EHR data identify high-AF-risk CVD patients, outperforming established clinical risk scores.

cs.LG↗

arXiv2025

HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for Computational Pathology

Recent advancements in Digital Pathology (DP), particularly through artificial intelligence and Foundation Models, have underscored the importance of large-scale, diverse, and richly annotated datasets. Despite their critical role, publicly available Whole Slide Image (WSI) datasets often lack sufficient scale, tissue diversity, and comprehensive clinical metadata, limiting the robustness and generalizability of AI models. In response, we introduce the HISTAI dataset, a large, multimodal, open-access WSI collection comprising over 60,000 slides from various tissue types. Each case in the HISTAI dataset is accompanied by extensive clinical metadata, including diagnosis, demographic information, detailed pathological annotations, and standardized diagnostic coding. The dataset aims to fill gaps identified in existing resources, promoting innovation, reproducibility, and the development of clinically relevant computational pathology solutions. The dataset can be accessed at https://github.com/HistAI/HISTAI.

eess.IV↗

arXiv2025

SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and Baseline Models

Advancing AI in computational pathology requires large, high-quality, and diverse datasets, yet existing public datasets are often limited in organ diversity, class coverage, or annotation quality. To bridge this gap, we introduce SPIDER (Supervised Pathology Image-DEscription Repository), the largest publicly available patch-level dataset covering multiple organ types, including Skin, Colorectal, Thorax, and Breast with comprehensive class coverage for each organ. SPIDER provides high-quality annotations verified by expert pathologists and includes surrounding context patches, which enhance classification performance by providing spatial context. Alongside the dataset, we present baseline models trained on SPIDER using the Hibou-L foundation model as a feature extractor combined with an attention-based classification head. The models achieve state-of-the-art performance across multiple tissue categories and serve as strong benchmarks for future digital pathology research. Beyond patch classification, the model enables rapid identification of significant areas, quantitative tissue metrics, and establishes a foundation for multimodal approaches. Both the dataset and trained models are publicly available to advance research, reproducibility, and AI-driven pathology development. Access them at: https://github.com/HistAI/SPIDER

eess.IV↗

arXiv2024

Human Impedance Modulation to Improve Visuo-Haptic Perception

Humans activate muscles to shape the mechanical interaction with their environment, but can they harness this control mechanism to best sense the environment? We investigated how participants adapt their muscle activation to visual and haptic information when tracking a randomly moving target with a robotic interface. The results exhibit a differentiated effect of these sensory modalities, where participants' muscle cocontraction increases with the haptic noise and decreases with the visual noise, in apparent contradiction to previous results. These results can be explained, and reconciled with previous findings, when considering muscle spring like mechanics, where stiffness increases with cocontraction to regulate motion guidance. Increasing cocontraction to more closely follow the motion plan favors accurate visual over haptic information, while decreasing it avoids injecting visual noise and relies on accurate haptic information. We formulated this active sensing mechanism as the optimization of visuo-haptic information and effort. This OIE model can explain the adaptation of muscle activity to unimodal and multimodal sensory information when interacting with fixed or dynamic environments, or with another human, and can be used to optimize human-robot interaction.

cs.HC↗

arXiv2024

Hibou: A Family of Foundational Vision Transformers for Pathology

Pathology, the microscopic examination of diseased tissue, is critical for diagnosing various medical conditions, particularly cancers. Traditional methods are labor-intensive and prone to human error. Digital pathology, which converts glass slides into high-resolution digital images for analysis by computer algorithms, revolutionizes the field by enhancing diagnostic accuracy, consistency, and efficiency through automated image analysis and large-scale data processing. Foundational transformer pretraining is crucial for developing robust, generalizable models as it enables learning from vast amounts of unannotated data. This paper introduces the Hibou family of foundational vision transformers for pathology, leveraging the DINOv2 framework to pretrain two model variants, Hibou-B and Hibou-L, on a proprietary dataset of over 1 million whole slide images (WSIs) representing diverse tissue types and staining techniques. Our pretrained models demonstrate superior performance on both patch-level and slide-level benchmarks, surpassing existing state-of-the-art methods. Notably, Hibou-L achieves the highest average accuracy across multiple benchmark datasets. To support further research and application in the field, we have open-sourced the Hibou models, which can be accessed at https://github.com/HistAI/hibou.

eess.IV↗

arXiv2021

Trimanipulation: Evaluation of human performance in a 3-handed coordination task

Many teleoperation tasks require three or more tools working together, which need the cooperation of multiple operators. The effectiveness of such schemes may be limited by communication. Trimanipulation by a single operator using an artificial third arm controlled together with their natural arms is a promising solution to this issue. Foot-controlled interfaces have previously shown the capability to be used for the continuous control of robot arms. However, the use of such interfaces for controlling a supernumerary robotic limb (SRLs) in coordination with the natural limbs, is not well understood. In this paper, a teleoperation task imitating physically coupled hands in a virtual reality scene was conducted with 14 subjects to evaluate human performance during tri-manipulation. The participants were required to move three limbs together in a coordinated way mimicking three arms holding a shared physical object. It was found that after a short practice session, the three-hand tri-manipulation using a single subject's hands and foot was still slower than dyad operation, however, they displayed similar performance in success rate and higher motion efficiency than two person's cooperation.

cs.RO↗