6 citations · 14 across the 10 of their papers we have counts for
11 papers
AI Consilium: a Benchmark of LLM Clinical Multidisciplinary Second Opinions
Alexey A. Bokov, Ivan I. Panin, Nikolay B. Nechaev +10
Large language models (LLMs) can generate differential diagnoses for complex cases, but a useful second opinion must also be factually grounded, clinically coherent, actionable, an…
EvoMem: Memory-Augmented Evolution for Code Optimization
Viktor Volkov, Valentin Khrulkov, Andrey V. Galichin +8
Successful mutation strategies in evolutionary code search may contain reusable knowledge that is useful beyond a single run, and in some cases may transfer across related tasks an…
Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports
Olga Shakhmatova, Dmitrii Kriukov, Daniil Larionov +10
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, hypertens…
LLM-Guided Evolution for Medical Decision Pipelines
Ivan Sviridov, Artem Oskin, Ivan Panin +4
Adapting large language models (LLMs) to clinical workflows often requires costly fine-tuning or manual prompt and pipeline engineering. We study LLM-guided MAP-Elites evolution as…
CLARITY: Clinical Assistant for Routing, Inference, and Triage
Vladimir Shaposhnikov, Aleksandr Nesterov, Ilia Kopanichuk +7
We present CLARITY (Clinical Assistant for Routing, Inference and Triage), an AI-driven platform designed to facilitate patient-to-specialist routing, clinical consultations, and s…
How to Evaluate Medical AI
Ilia Kopanichuk, Petr Anokhin, Vladimir Shaposhnikov +5
The integration of artificial intelligence (AI) into medical diagnostic workflows requires robust and consistent evaluation methods to ensure reliability, clinical relevance, and t…
FLAME: Flexible LLM-Assisted Moderation Engine
Ivan Bakulin, Ilia Kopanichuk, Iaroslav Bespalov +4
The rapid advancement of Large Language Models (LLMs) has introduced significant challenges in moderating user-model interactions. While LLMs demonstrate remarkable capabilities, t…
Feather-Light Fourier Domain Adaptation in Magnetic Resonance Imaging
Ivan Zakazov, Vladimir Shaposhnikov, Iaroslav Bespalov +1
Generalizability of deep learning models may be severely affected by the difference in the distributions of the train (source domain) and the test (target domain) sets, e.g., when…
Landmarks Augmentation with Manifold-Barycentric Oversampling
Iaroslav Bespalov, Nazar Buzun, Oleg Kachan +1
The training of Generative Adversarial Networks (GANs) requires a large amount of data, stimulating the development of new augmentation methods to alleviate the challenge. Oftentim…
Global Adaptive Filtering Layer for Computer Vision
Viktor Shipitsin, Iaroslav Bespalov, Dmitry V. Dylov
We devise a universal adaptive neural layer to "learn" optimal frequency filter for each image together with the weights of the base neural network that performs some computer visi…
BRULÈ: Barycenter-Regularized Unsupervised Landmark Extraction
Iaroslav Bespalov, Nazar Buzun, Dmitry V. Dylov
Unsupervised retrieval of image features is vital for many computer vision tasks where the annotation is missing or scarce. In this work, we propose a new unsupervised approach to…