Titlebook: Medical Image Understanding and Analysis; 24th Annual Conferen Bartłomiej W. Papież,Ana I. L. Namburete,J. Alison Conference proceedings 20

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Fibrillation

遗产

Conference proceedings 2020ubmissions. They were organized according to following topical sections: ​image segmentation; image registration, reconstruction and enhancement; radiomics, predictive models, and quantitative imaging biomarkers; ocular imaging analysis; biomedical simulation and modelling..

Badger

Conference proceedings 2020o COVID-19 pandemic the conference was held virtually. .The 29 full papers and 5 short papers presented were carefully reviewed and selected from 70 submissions. They were organized according to following topical sections: ​image segmentation; image registration, reconstruction and enhancement; radi

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Haphazard

Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Entation map and the original image, we are able to predict if a patient has MF or Eczema. We created two models that can be applied in different stages of the diagnostic pipeline, potentially eliminating life-threatening mistakes. The classification outcome is considerably more interpretable than us

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Autofocus Net: Auto-focused 3D CNN for Brain Tumour Segmentationt dilation rate. We replaced standard convolutional layers with autofocus layers to adaptively change the size of the effective receptive field to generate more powerful features. Experiments with our autofocus settings on the BraTS 2018 glioma dataset show that the proposed method achieved average

SOB

Improving U-Net Segmentation with Active Contour Based Label Correctionon the segmentation of the left ventricle in 2D ultrasound scans. The active contour label correction yielded more precise boundary predictions, suggesting that this simple correction step can improve boundary segmentation with imperfect labels.

bibliophile

Segmentation of the Biliary Tree from MRCP Images via the Monogenic Signalbile ducts and obtain accurate duct diameter measurements. Compared to the Hessian-based Frangi vesselness filter, we show that our method gives superior background noise suppression and performs better at duct bifurcations, where the model assumptions underlying vesselness fail.

HEW

Transfer Learning for Brain Segmentation: Pre-task Selection and Data Limitationscan achieve state-of-the-art performance. Further, this pre-training task utilises automated labels, meaning the pipeline requires very few manually segmented data points. On the other hand, using a different task for pre-training is shown to be less successful. We then conclude, by showing that, wh