6 papers
Self-supervised Physics-based Denoising for Computed Tomography
Elvira Zainulina, Alexey Chernyavskiy, Dmitry V. Dylov
Computed Tomography (CT) imposes risk on the patients due to its inherent X-ray radiation, stimulating the development of low-dose CT (LDCT) imaging methods. Lowering the radiation…
Medical image segmentation with imperfect 3D bounding boxes
Ekaterina Redekop, Alexey Chernyavskiy
The development of high quality medical image segmentation algorithms depends on the availability of large datasets with pixel-level labels. The challenges of collecting such datas…
Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation
Ivan Zakazov, Boris Shirokikh, Alexey Chernyavskiy +1
Domain Adaptation (DA) methods are widely used in medical image segmentation tasks to tackle the problem of differently distributed train (source) and test (target) data. We consid…
Uncertainty-based method for improving poorly labeled segmentation datasets
Ekaterina Redekop, Alexey Chernyavskiy
The success of modern deep learning algorithms for image segmentation heavily depends on the availability of large datasets with clean pixel-level annotations (masks), where the ob…
No-reference denoising of low-dose CT projections
Elvira Zainulina, Alexey Chernyavskiy, Dmitry V. Dylov
Low-dose computed tomography (LDCT) became a clear trend in radiology with an aspiration to refrain from delivering excessive X-ray radiation to the patients. The reduction of the…
First U-Net Layers Contain More Domain Specific Information Than The Last Ones
Boris Shirokikh, Ivan Zakazov, Alexey Chernyavskiy +2
MRI scans appearance significantly depends on scanning protocols and, consequently, the data-collection institution. These variations between clinical sites result in dramatic drop…