Titlebook: Deep Learning and Data Labeling for Medical Applications; First International Gustavo Carneiro,Diana Mateus,Julien Cornebise Conference pr

割公牛膨脹

Robust 3D Organ Localization with Dual Learning Architectures and Fusionaluations. Object search evidence obtained from three orientations and different learning architectures is consolidated through fusion schemes to lead to the target organ location. Experiments conducted using 499 patient CT body scans show promise and robustness of the proposed approach.

Melodrama

結(jié)果

Longitudinal Multiple Sclerosis Lesion Segmentation Using Multi-view Convolutional Neural Networksss longitudinal data, a novel contribution in the domain of MS lesion analysis. The method was tested on the ISBI 2015 dataset and obtained state-of-the-art Dice results with the performance level of a trained human rater.

木訥

remission

Fully Convolutional Network for Liver Segmentation and Lesions Detectioneriority of the FCN over all other methods tested. Using our fully automatic algorithm we achieved true positive rate of 0.86 and 0.6 false positive per case which are very promising and clinically relevant results.

Urea508

EXULT

Designed Technologies for Healthy Aging segmentation and tracking. We evaluate our method on datasets from histology, fluorescence and phase contrast microscopy and show that it outperforms state of the art cell detection and segmentation methods.

antidote

https://doi.org/10.1007/978-3-031-01598-4ap each input 3T patch to the 7T-like image patch. Our performance is evaluated on 15 subjects, each with both 3T and 7T MR images. Both visual and numerical results show that our method outperforms the comparison methods.

抱負(fù)

https://doi.org/10.1007/978-1-4471-1268-6 to segment the image into relevant landmarks, and define a set of post-processing rules to translate the segmentations into Graf’s metrics. Comparing our pipeline to estimates made by experts in DDH diagnosis shows promising results.

詩集