To tackle this deficiency, we propose Contrastive Domain Disentangle (CDD) network for generalizable medical image segmentation.
Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation.
First, we present a domain composition method that represents one certain domain by a linear combination of a set of basis representations (i. e., a representation bank).
To solve this problem, we propose a Semi-Supervised Cross-Anatomy Domain Adaptation (SS-CADA) which requires only limited annotations for coronary arteries in XAs.
Despite the stateof-the-art performance achieved by Convolutional Neural Networks (CNNs) for automatic segmentation of OARs, existing methods do not provide uncertainty estimation of the segmentation results for treatment planning, and their accuracy is still limited by several factors, including the low contrast of soft tissues in CT, highly imbalanced sizes of OARs and large inter-slice spacing.
Delineation of Gross Target Volume (GTV) from medical images such as CT and MRI images is a prerequisite for radiotherapy.
In the second stage, an interior point method is adopted to accelerate the local convergence.
Optimization and Control Numerical Analysis Numerical Analysis 65K10, 90C26 G.1.6; F.2.1
To address this problem, we present a one-shot framework for organ and landmark localization in volumetric medical images, which does not need any annotation during the training stage and could be employed to locate any landmarks or organs in test images given a support (reference) image during the inference stage.
They also proved that every digraph on at most 6 vertices and arc-connectivity at least 2 has a good pair and gave an example of a 2-arc-strong digraph $D$ on 10 vertices with independence number 4 that has no good pair.
1 code implementation • 22 Oct 2020 • Chia-Hao Liu, Christopher J. Wright, Ran Gu, Sasaank Bandi, Allison Wustrow, Paul K. Todd, Daniel O'Nolan, Michelle L. Beauvais, James R. Neilson, Peter J. Chupas, Karena W. Chapman, Simon J. L. Billinge
We validate the use of matrix factorization for the automatic identification of relevant components from atomic pair distribution function (PDF) data.
Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object.