37 citations · 39 across the 6 of their papers we have counts for
7 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…
The impact of deep learning aid on the workload and interpretation accuracy of radiologists on chest computed tomography: a cross-over reader study
Anvar Kurmukov, Valeria Chernina, Regina Gareeva +18
Interpretation of chest computed tomography (CT) is time-consuming. Previous studies have measured the time-saving effect of using a deep-learning-based aid (DLA) for CT interpreta…
Interpretable Vertebral Fracture Quantification via Anchor-Free Landmarks Localization
Alexey Zakharov, Maxim Pisov, Alim Bukharaev +4
Vertebral body compression fractures are early signs of osteoporosis. Though these fractures are visible on Computed Tomography (CT) images, they are frequently missed by radiologi…
CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification
Mikhail Goncharov, Maxim Pisov, Alexey Shevtsov +8
The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody…
MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset
S. P. Morozov, A. E. Andreychenko, N. A. Pavlov +7
This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been anno…
Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification
Maxim Pisov, Vladimir Kondratenko, Alexey Zakharov +4
Vertebral body compression fractures are reliable early signs of osteoporosis. Though these fractures are visible on Computed Tomography (CT) images, they are frequently missed by…
Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection
Maxim Pisov, Mikhail Goncharov, Nadezhda Kurochkina +9
Midline shift (MLS) is a well-established factor used for outcome prediction in traumatic brain injury, stroke and brain tumors. The importance of automatic estimation of MLS was r…