TY - GEN
T1 - Airway Anomaly Detection by Prototype-Based Graph Neural Network
AU - Zhao, Tianyi
AU - Yin, Zhaozheng
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Detecting the airway anomaly can be an essential part to aid the lung disease diagnosis. Since normal human airways share an anatomical structure, we design a graph prototype whose structure follows the normal airway anatomy. Then, we learn the prototype and a graph neural network from a weakly-supervised airway dataset, i.e., only the holistic label is available, indicating if the airway has anomaly or not, but which bronchus node has the anomaly is unknown. During inference, the graph neural network predicts the anomaly score at both the holistic level and node-level of an airway. We initialize the airway anomaly detection problem by creating a large airway dataset with 2589 samples, and our prototype-based graph neural network shows high sensitivity and specificity on this new benchmark dataset. The code is available at https://github.com/tznbz/Airway-Anomaly-Detection-by-Prototype-based-Graph-Neural-Network.
AB - Detecting the airway anomaly can be an essential part to aid the lung disease diagnosis. Since normal human airways share an anatomical structure, we design a graph prototype whose structure follows the normal airway anatomy. Then, we learn the prototype and a graph neural network from a weakly-supervised airway dataset, i.e., only the holistic label is available, indicating if the airway has anomaly or not, but which bronchus node has the anomaly is unknown. During inference, the graph neural network predicts the anomaly score at both the holistic level and node-level of an airway. We initialize the airway anomaly detection problem by creating a large airway dataset with 2589 samples, and our prototype-based graph neural network shows high sensitivity and specificity on this new benchmark dataset. The code is available at https://github.com/tznbz/Airway-Anomaly-Detection-by-Prototype-based-Graph-Neural-Network.
KW - Anomaly detection
KW - Bronchus classification
KW - Graph neural network
UR - https://www.scopus.com/pages/publications/85116421164
U2 - 10.1007/978-3-030-87240-3_19
DO - 10.1007/978-3-030-87240-3_19
M3 - Conference contribution
AN - SCOPUS:85116421164
SN - 9783030872397
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 195
EP - 204
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
A2 - de Bruijne, Marleen
A2 - Cattin, Philippe C.
A2 - Cotin, Stéphane
A2 - Padoy, Nicolas
A2 - Speidel, Stefanie
A2 - Zheng, Yefeng
A2 - Essert, Caroline
PB - Springer Science and Business Media Deutschland GmbH
T2 - 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
Y2 - 27 September 2021 through 1 October 2021
ER -