Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Chernyavskiy, Alexey"'
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 dose reduces the health risks but leads to noisier measurements, whic
Externí odkaz:
http://arxiv.org/abs/2211.00745
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 datasets, especially in case of 3D volumes, motivate to develop approaches
Externí odkaz:
http://arxiv.org/abs/2108.03300
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 consider the supervised DA task with a limited number of annotated samples f
Externí odkaz:
http://arxiv.org/abs/2107.04914
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 objects of interest are accurately delineated. Lack of time and expertis
Externí odkaz:
http://arxiv.org/abs/2102.08021
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 radiation dose decreases the risks to the patients but raises the nois
Externí odkaz:
http://arxiv.org/abs/2102.02662
MRI scans appearance significantly depends on scanning protocols and, consequently, the data-collection institution. These variations between clinical sites result in dramatic drops of CNN segmentation quality on unseen domains. Many of the recently
Externí odkaz:
http://arxiv.org/abs/2008.07357