TY - GEN
T1 - Latent Feature Regularization based Adversarial Network for Brain Tumor Anomaly Detection
AU - Wang, Nan
AU - Chen, Chengwei
AU - Ma, Lizhuang
AU - Lin, Shaohui
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Brain tumor anomaly detection plays a critical role in the field of computer-aided diagnosis, which has attracted ever-increasing focus from the medical community However, brain tumor data are scarce and tough to classify. Unsupervised methods enable the reduction of huge labeling costs to be applied to brain tumor anomaly detection during the training only given normal brain images. However, the existing unsupervised methods distinguish whether the input image is abnormal in the image space, which cannot effectively learn the discriminative features. In this paper, we propose a novel brain tumor anomaly detection method via Latent Feature Regularization based Adversarial Network (LFRA-Net), which leverages a latent feature regularizer into adversarial learning to obtain the discriminative features. Comprehensive experiments on BraTS, HCP, MNIST, and CIFAR-10 datasets evaluate the effectiveness of our LFRANet, which outperforms state-of-the-art unsupervised learning methods.
AB - Brain tumor anomaly detection plays a critical role in the field of computer-aided diagnosis, which has attracted ever-increasing focus from the medical community However, brain tumor data are scarce and tough to classify. Unsupervised methods enable the reduction of huge labeling costs to be applied to brain tumor anomaly detection during the training only given normal brain images. However, the existing unsupervised methods distinguish whether the input image is abnormal in the image space, which cannot effectively learn the discriminative features. In this paper, we propose a novel brain tumor anomaly detection method via Latent Feature Regularization based Adversarial Network (LFRA-Net), which leverages a latent feature regularizer into adversarial learning to obtain the discriminative features. Comprehensive experiments on BraTS, HCP, MNIST, and CIFAR-10 datasets evaluate the effectiveness of our LFRANet, which outperforms state-of-the-art unsupervised learning methods.
KW - Anomaly detection
KW - adversarial learning
KW - latent feature regularization
KW - multi-model medical image
UR - https://www.scopus.com/pages/publications/85171172620
U2 - 10.1109/ICME55011.2023.00168
DO - 10.1109/ICME55011.2023.00168
M3 - 会议稿件
AN - SCOPUS:85171172620
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
SP - 954
EP - 959
BT - Proceedings - 2023 IEEE International Conference on Multimedia and Expo, ICME 2023
PB - IEEE Computer Society
T2 - 2023 IEEE International Conference on Multimedia and Expo, ICME 2023
Y2 - 10 July 2023 through 14 July 2023
ER -