Titlebook: Interpretable and Annotation-Efficient Learning for Medical Image Computing; Third International Jaime Cardoso,Hien Van Nguyen,Samaneh Abb

lanugo

作茧自缚

ferment

Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteinssible only temporarily, existing frame-by-frame methods fail. In this paper, we provide a solution to segmentation of imperfect data through time based on temporal propagation and uncertainty estimation. We integrate uncertainty estimation into Mask R-CNN network and propagate motion-corrected segme

OTHER

adumbrate

他日关税重重

Semi-supervised Machine Learning with MixMatch and Equivalence Classesot been well translated to medical imaging. Of particular interest, the MixMatch method achieves significant performance improvement over popular semi-supervised learning methods with scarce labels in the CIFAR-10 dataset. In a complementary approach, Nullspace Tuning on equivalence classes offers t

背叛者

Non-contrast CT Liver Segmentation Using CycleGAN Data Augmentation from Contrast Enhanced CTdaries and scarce supervised training data than contrast-enhanced CT (CTce) segmentation. To alleviate manual labelling work of radiologists, we generate training samples for 3D U-Net segmentation network by transforming the existing CTce liver segmentation dataset to the non-contrast CT styled volu

欲望

Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for, but these are known to lack robustness when anatomic differences between atlases and subjects are large. To improve the localization robustness, we propose a novel two-stage deep learning (DL) framework, where the first stage identifies and crops the thalamus regions from the whole brain MRI and t

财主

A Case Study of Transfer of Lesion-Knowledgeng with the acknowledged ability of neural-network methods to analyse image data, would suggest that accurate models for lesions can now be constructed by a deep neural network. However an important difficulty arises from the lack of annotated images from various parts of the body. Our proposed appr

TAP