1 citations · 1 across the 5 of their papers we have counts for
6 papers
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs
Mikhail Aleksandrov, Nail Bashirov, Maksim Ivanov +6
Prompt-based adaptation offers several ways to specialize a frozen language model, ranging from natural-language prompt optimization to learned continuous prompts. These methods ar…
Random Direct Preference Optimization for Radiography Report Generation
Valentin Samokhin, Boris Shirokikh, Mikhail Goncharov +5
Radiography Report Generation (RRG) has gained significant attention in medical image analysis as a promising tool for alleviating the growing workload of radiologists. However, de…
M3DA: Benchmark for Unsupervised Domain Adaptation in 3D Medical Image Segmentation
Boris Shirokikh, Anvar Kurmukov, Mariia Donskova +3
Domain shift presents a significant challenge in applying Deep Learning to the segmentation of 3D medical images from sources like Magnetic Resonance Imaging (MRI) and Computed Tom…
Anatomical Positional Embeddings
Mikhail Goncharov, Valentin Samokhin, Eugenia Soboleva +5
We propose a self-supervised model producing 3D anatomical positional embeddings (APE) of individual medical image voxels. APE encodes voxels' anatomical closeness, i.e., voxels of…
Robust Curve Detection in Volumetric Medical Imaging via Attraction Field
Farukh Yaushev, Daria Nogina, Valentin Samokhin +5
Understanding body part geometry is crucial for precise medical diagnostics. Curves effectively describe anatomical structures and are widely used in medical imaging applications r…
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…