Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis
Springer International Publishing (Verlag)
978-3-030-60364-9 (ISBN)
For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world.
GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.
UNSURE 2020.- Image registration via stochastic gradient Markov chain Monte Carlo.- RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification.- Hierarchical brain parcellation with uncertainty.- Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-Class Segmentation.- Uncertainty Estimation in Landmark Localization based on Gaussian Heatmaps.- Weight averaging impact on the uncertainty of retinal artery-venous segmentation.- Improving Pathological Distribution Measurements with Bayesian Uncertainty.- Improving Reliability of Clinical Models using Prediction Calibration.- Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior.- Uncertainty Estimation for Assessment of 3D US Scan Adequacy and DDH Metric Reliability.- GRAIL 2020.- Clustering-based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates.- Detection of Discriminative Neurological Circuits Using Hierarchical GraphConvolutional Networks in fMRI Sequences.- Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders.- Multi-Scale Profiling of Brain Multigraphs by Eigen-based Cross-Diffusion and Heat Tracing for Brain State Proling.- Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation.- Min-cut Max-flow for Network Abnormality Detection: Application to Preterm Birth.- Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface.- The GraphNet Zoo: An All-in-One Graph Based Deep Semi-Supervised Framework for Medical Image Classification.- Intraoperative Liver Surface Completion with Graph Convolutional VAE.- HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification.
| Erscheinungsdatum | 07.10.2020 |
|---|---|
| Reihe/Serie | Image Processing, Computer Vision, Pattern Recognition, and Graphics | Lecture Notes in Computer Science |
| Zusatzinfo | XVII, 222 p. 85 illus., 76 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 373 g |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Sozialwissenschaften | |
| Schlagworte | Applications • Artificial Intelligence • Bioinformatics • Computer Science • computer vision • conference proceedings • Deep learning • graph theory • Image Analysis • Image Processing • image reconstruction • Image Segmentation • Informatics • machine learning • Medical Images • Neural networks • pattern recognition • Research • Signal Processing |
| ISBN-10 | 3-030-60364-4 / 3030603644 |
| ISBN-13 | 978-3-030-60364-9 / 9783030603649 |
| Zustand | Neuware |
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