Zobrazeno 1 - 10
of 241
pro vyhledávání: '"BELYAEV, Mikhail"'
Autor:
Goncharov, Mikhail, Samokhin, Valentin, Soboleva, Eugenia, Sokolov, Roman, Shirokikh, Boris, Belyaev, Mikhail, Kurmukov, Anvar, Oseledets, Ivan
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 the same organ or nearby organs always have closer positional embeddi
Externí odkaz:
http://arxiv.org/abs/2409.10291
Autor:
Yaushev, Farukh, Nogina, Daria, Samokhin, Valentin, Dugova, Mariya, Petrash, Ekaterina, Sevryukov, Dmitry, Belyaev, Mikhail, Pisov, Maxim
Understanding body part geometry is crucial for precise medical diagnostics. Curves effectively describe anatomical structures and are widely used in medical imaging applications related to cardiovascular, respiratory, and skeletal diseases. Traditio
Externí odkaz:
http://arxiv.org/abs/2408.01159
Autor:
Kurmukov, Anvar, Chernina, Valeria, Gareeva, Regina, Dugova, Maria, Petrash, Ekaterina, Aleshina, Olga, Pisov, Maxim, Shirokikh, Boris, Samokhin, Valentin, Proskurov, Vladislav, Shimovolos, Stanislav, Basova, Maria, Goncahrov, Mikhail, Soboleva, Eugenia, Donskova, Maria, Yaushev, Farukh, Shevtsov, Alexey, Zakharov, Alexey, Saparov, Talgat, Gombolevskiy, Victor, Belyaev, Mikhail
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 interpretation. We evaluated the joint impact of a multi-pathology DLA on the ti
Externí odkaz:
http://arxiv.org/abs/2406.08137
Autor:
Leonov, Aleksei, Zakharov, Aleksei, Koshelev, Sergey, Pisov, Maxim, Kurmukov, Anvar, Belyaev, Mikhail
Automatic ribs segmentation and numeration can increase computed tomography assessment speed and reduce radiologists mistakes. We introduce a model for multilabel ribs segmentation with hierarchical loss function, which enable to improve multilabel s
Externí odkaz:
http://arxiv.org/abs/2405.15500
Detecting out-of-distribution (OOD) samples for trusted medical image segmentation remains a significant challenge. The critical issue here is the lack of a strict definition of abnormal data, which often results in artificial problem settings withou
Externí odkaz:
http://arxiv.org/abs/2308.07324
This paper introduces vox2vec - a contrastive method for self-supervised learning (SSL) of voxel-level representations. vox2vec representations are modeled by a Feature Pyramid Network (FPN): a voxel representation is a concatenation of the correspon
Externí odkaz:
http://arxiv.org/abs/2307.14725
Deep Learning models perform unreliably when the data comes from a distribution different from the training one. In critical applications such as medical imaging, out-of-distribution (OOD) detection methods help to identify such data samples, prevent
Externí odkaz:
http://arxiv.org/abs/2306.13528
Deep Learning (DL) models tend to perform poorly when the data comes from a distribution different from the training one. In critical applications such as medical imaging, out-of-distribution (OOD) detection helps to identify such data samples, incre
Externí odkaz:
http://arxiv.org/abs/2212.06506
When applying a Deep Learning model to medical images, it is crucial to estimate the model uncertainty. Voxel-wise uncertainty is a useful visual marker for human experts and could be used to improve the model's voxel-wise output, such as segmentatio
Externí odkaz:
http://arxiv.org/abs/2211.00303
Autor:
Zakharov, Alexey, Pisov, Maxim, Bukharaev, Alim, Petraikin, Alexey, Morozov, Sergey, Gombolevskiy, Victor, Belyaev, Mikhail
Vertebral body compression fractures are early signs of osteoporosis. Though these fractures are visible on Computed Tomography (CT) images, they are frequently missed by radiologists in clinical settings. Prior research on automatic methods of verte
Externí odkaz:
http://arxiv.org/abs/2204.06818