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Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 Sensors  
We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare.  ...  [24] combined both the fundus image sequence and FA image as input for artery and vein classification.  ... 
doi:10.3390/s21144758 fatcat:jytyt4u2pjgvhnhcto3vcvd3a4

2021 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 43

2022 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  ., +, TPAMI Dec. 2021 4426-4440 Supervision by Registration and Triangulation for Landmark Detection.  ... 
doi:10.1109/tpami.2021.3126216 fatcat:h6bdbf2tdngefjgj76cudpoyia

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare.  ...  learning techniques to automatically detecting anomalies in medical data is particularly attractive considering the difficulties in consistency and objectivity identifying anomalies.  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

The Liver Tumor Segmentation Benchmark (LiTS) [article]

Patrick Bilic, Patrick Ferdinand Christ, Eugene Vorontsov, Grzegorz Chlebus, Hao Chen, Qi Dou, Chi-Wing Fu, Xiao Han, Pheng-Ann Heng, Jürgen Hesser, Samuel Kadoury, Tomasz Konopczyǹski (+44 others)
2019 arXiv   pre-print
We found that not a single algorithm performed best for liver and tumors.  ...  The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI).  ...  Level set approaches for liver tumor segmentation are combined with supervised pixel classification in 2D [80] and 3D [81] .  ... 
arXiv:1901.04056v1 fatcat:25ekt2znl5adnd5laap4ez6a4y

Automated organ localisation in fetal Magnetic Resonance Imaging

Kevin Keraudren, Daniel Rueckert, Engineering And Physical Sciences Research Council
2016
The fetal brain is first localised independently of the orientation of the fetus, and then used as an anchor point to steer features used in the subsequent localisation of the heart, lungs and liver.  ...  The proposed method to segment the fetal brain is shown to perform as well as a manual preprocessing.  ...  For the dataset 3, the largest error for the detection of the brain using the method presented in Chapter 4 is 21mm, which is inside the brain and is sufficient for the proposed method to succeed.  ... 
doi:10.25560/29432 fatcat:ddzbk5gicvetxoxauuoavi2g7a

Registration and analysis of dynamic magnetic resonance image series

Robin Sandkühler, Philippe Cattin, Philipp Latzin, Ivana Išgum, Christoph Jud
2020 unpublished
Acknowledgements We would like to thank Oliver Bieri, Orso Pusterla ( Acknowledgments The authors thank Alina Giger, Reinhard Wendler, and Simon Pezold for their great support.  ...  Acknowledgement The authors would like to thank the Swiss National Science Foundation for funding this project (SNF 320030 149576).  ...  Unsupervised methods are used for medical segmentation tasks are also known as anomaly detection or out of distribution detection (ODD).  ... 
doi:10.5451/unibas-007218484 fatcat:plcm6xskprcw3dbi6a5k5hrnf4

Table of Contents

2021 2021 40th Chinese Control Conference (CCC)   unpublished
ZHUANG Shao, CHEN Chen 3397 Unsupervised Time Series Anomaly Detection Using Moving Memorial Dynamic Filter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  WANG Zhiyue, ZHOU Wei, LI Junlin, CHEN Zhiyong, CHEN Shifeng, ZHANG Hai-Tao 6880 Detection of Smart Meter Anomaly with Error Tolerance Based on Tikhonov Regularization . . . . . . . . . . . . . . . . .  ... 
doi:10.23919/ccc52363.2021.9550117 fatcat:55y7a2gagfhtpc6llmfvl7gqpm