default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDUNSURE/GRAIL@MICCAI 2020: Lima, Peru
- Carole H. Sudre
, Hamid Fehri, Tal Arbel, Christian F. Baumgartner, Adrian V. Dalca, Ryutaro Tanno, Koen Van Leemput, William M. Wells III, Aristeidis Sotiras, Bartlomiej W. Papiez, Enzo Ferrante, Sarah Parisot:
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis - Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings. Lecture Notes in Computer Science 12443, Springer 2020, ISBN 978-3-030-60364-9
UNSURE 2020
- Daniel Grzech, Bernhard Kainz, Ben Glocker, Loïc Le Folgoc:
Image Registration via Stochastic Gradient Markov Chain Monte Carlo. 3-12 - Marc Gantenbein, Ertunc Erdil, Ender Konukoglu:
RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation. 13-22 - Mark S. Graham, Carole H. Sudre, Thomas Varsavsky, Petru-Daniel Tudosiu, Parashkev Nachev, Sébastien Ourselin, Manuel Jorge Cardoso:
Hierarchical Brain Parcellation with Uncertainty. 23-31 - Robin Camarasa, Daniel Bos, Jeroen Hendrikse, Paul H. J. Nederkoorn, M. Eline Kooi, Aad van der Lugt, Marleen de Bruijne:
Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-class Segmentation. 32-41 - Christian Payer, Martin Urschler, Horst Bischof, Darko Stern:
Uncertainty Estimation in Landmark Localization Based on Gaussian Heatmaps. 42-51 - Markus Lindén, Azat Garifullin, Lasse Lensu:
Weight Averaging Impact on the Uncertainty of Retinal Artery-Venous Segmentation. 52-60 - Ka Ho Tam, Korsuk Sirinukunwattana, Maria F. Soares, Maria Kaisar, Rutger Ploeg, Jens Rittscher:
Improving Pathological Distribution Measurements with Bayesian Uncertainty. 61-70 - Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan, Prasanna Sattigeri:
Improving Reliability of Clinical Models Using Prediction Calibration. 71-80 - Max-Heinrich Laves, Malte Tölle, Tobias Ortmaier:
Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior. 81-96 - Arunkumar Kannan, Antony J. Hodgson, Kishore Mulpuri, Rafeef Garbi:
Uncertainty Estimation for Assessment of 3D US Scan Adequacy and DDH Metric Reliability. 97-105
GRAIL 2020
- Ugur Demir, Mohammed Amine Gharsallaoui, Islem Rekik:
Clustering-Based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates. 109-120 - Xiaodan Xing, Lili Jin, Qinfeng Li, Lei Chen, Zhong Xue, Ziwen Peng, Feng Shi, Dinggang Shen:
Detection of Discriminative Neurological Circuits Using Hierarchical Graph Convolutional Networks in fMRI Sequences. 121-130 - Rui Sherry Shen, Jacob A. Alappatt, Drew Parker, Junghoon Kim, Ragini Verma, Yusuf Osmanlioglu:
Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders. 131-141 - Mustafa Saglam, Islem Rekik:
Multi-scale Profiling of Brain Multigraphs by Eigen-Based Cross-diffusion and Heat Tracing for Brain State Profiling. 142-151 - Karthik Gopinath, Christian Desrosiers, Herve Lombaert:
Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation. 152-163 - Hassna Irzan, Lucas Fidon, Tom Vercauteren, Sébastien Ourselin, Neil Marlow, Andrew Melbourne:
Min-Cut Max-Flow for Network Abnormality Detection: Application to Preterm Birth. 164-173 - Vitalis Vosylius, Andy Wang, Cemlyn Waters, Alexey Zakharov, Francis Ward, Loïc Le Folgoc, John Cupitt, Antonios Makropoulos, Andreas Schuh, Daniel Rueckert, Amir Alansary:
Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface. 174-186 - Marianne de Vriendt, Philip Sellars, Angelica I. Avilés-Rivero:
The GraphNet Zoo: An All-in-One Graph Based Deep Semi-supervised Framework for Medical Image Classification. 187-197 - Simone Foti, Bongjin Koo, Thomas Dowrick, João Ramalhinho, Moustafa Allam, Brian R. Davidson, Danail Stoyanov, Matthew J. Clarkson:
Intraoperative Liver Surface Completion with Graph Convolutional VAE. 198-207 - Pushpak Pati, Guillaume Jaume, Lauren Alisha Fernandes, Antonio Foncubierta-Rodríguez, Florinda Feroce, Anna Maria Anniciello, Giosue Scognamiglio, Nadia Brancati, Daniel Riccio, Maurizio Di Bonito, Giuseppe De Pietro, Gerardo Botti, Orcun Goksel, Jean-Philippe Thiran, Maria Frucci, Maria Gabrani:
HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification. 208-219
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2025-01-29 21:03 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
