Titlebook: Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data a; 7th Joint Internatio Danail Stoyanov,

隐语

Prediction of FFR from IVUS Images Using Machine Learningal patient informarion, we trained random forest (RF), extreme gradient boost (XGBoost) and artificial neural network (ANN) models for a binary classification of FFR-80 threshold (FFR < 0.8 v.s. FFR . 0.8) for comparison. The ensembled XGBoost models evaluated in 290 unseen cases achieved 81% accuracy and 70% recall.

强壮

Deep Learning Retinal Vessel Segmentation from a Single Annotated Example: An Application of Cyclic ive adversarial neural networks. The paper further shows that a fully convolutional network trained on a dataset of several synthetic examples and a single manually-crafted ground truth segmentation can approach the accuracy of an equivalent network trained on twenty manually segmented examples.

delta-waves

An Efficient and Comprehensive Labeling Tool for Large-Scale Annotation of Fundus Imagesional encoding. We have used our tool in a large-scale data annotation project in which 318,376 annotations for 109,885 fundus images were gathered with a total duration of 421 h. We believe that the fundamental concepts in our tool would inspire other data collection processes and annotation procedure in different domains.

条街道往前推

Crowd Disagreement About Medical Images Is Informativeean annotations, illustrating consensus, to standard deviations and other distribution moments, illustrating disagreement. We show that the mean annotations perform best, but that the disagreement measures are still informative. We also make the crowd annotations used in this paper available at ..

BIPED

0302-9743 ar Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the Third International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MIC

火花

Mortal

Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data a978-3-030-01364-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

侵略主义

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/i/image/473238.jpg

现任者

https://doi.org/10.1007/978-3-030-01364-6Artificial intelligence; Automatic segmentations; Classification; Image analysis; Image processing; Image

陶器

Jaemin Son,Sangkeun Kim,Sang Jun Park,Kyu-Hwan Jung (.MLCT) excited state is lowest in energy. The pronounced solvatochromic and rigidochromic effects of the Ir. compounds are responsible for the reversal of the order of the lowest excited states. Mixing between the π-π* and MLCT excited states is reflected in the oscillator strengths, luminescence