TY - GEN
T1 - CVAD
T2 - 1st International Workshop on Medical Image Learning with Limited and Noisy Data, MILLanD 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
AU - Guo, Xiaoyuan
AU - Gichoya, Judy Wawira
AU - Purkayastha, Saptarshi
AU - Banerjee, Imon
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Anomaly detection in medical imaging plays an important role to ensure AI generalization. However, existing out-of-distribution (OOD) detection approaches fail to account for OOD data granularity in medical images, where identifying both intra-class and inter-class OOD data is essential to the generalizability in the medical domain. We focus on the generalizability of outlier detection for medical images and propose a generic Cascade Variational autoencoder-based Anomaly Detector (CVAD). We use variational autoencoders’ cascade architecture, which combines latent representation at multiple scales, before being fed to a discriminator to distinguish the OOD data from the in-distribution data. Finally, both the reconstruction error and the OOD probability predicted by the binary discriminator are used to determine the anomalies. We compare the performance with the state-of-the-art deep learning models to demonstrate our model’s efficacy on various open-access natural and medical imaging datasets for intra- and inter-class OOD. Extensive experimental results on multiple datasets show our model’s effectiveness and generalizability. The code will be publicly available.
AB - Anomaly detection in medical imaging plays an important role to ensure AI generalization. However, existing out-of-distribution (OOD) detection approaches fail to account for OOD data granularity in medical images, where identifying both intra-class and inter-class OOD data is essential to the generalizability in the medical domain. We focus on the generalizability of outlier detection for medical images and propose a generic Cascade Variational autoencoder-based Anomaly Detector (CVAD). We use variational autoencoders’ cascade architecture, which combines latent representation at multiple scales, before being fed to a discriminator to distinguish the OOD data from the in-distribution data. Finally, both the reconstruction error and the OOD probability predicted by the binary discriminator are used to determine the anomalies. We compare the performance with the state-of-the-art deep learning models to demonstrate our model’s efficacy on various open-access natural and medical imaging datasets for intra- and inter-class OOD. Extensive experimental results on multiple datasets show our model’s effectiveness and generalizability. The code will be publicly available.
KW - Anomaly detection
KW - Cascade Variational autoencoder
KW - Medical images
KW - OOD detection
UR - http://www.scopus.com/inward/record.url?scp=85140448506&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85140448506&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16760-7_18
DO - 10.1007/978-3-031-16760-7_18
M3 - Conference contribution
AN - SCOPUS:85140448506
SN - 9783031167591
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 187
EP - 196
BT - Medical Image Learning with Limited and Noisy Data - 1st International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Proceedings
A2 - Zamzmi, Ghada
A2 - Antani, Sameer
A2 - Rajaraman, Sivaramakrishnan
A2 - Xue, Zhiyun
A2 - Bagci, Ulas
A2 - Linguraru, Marius George
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 September 2022 through 22 September 2022
ER -