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Enhanced Masked Image Modeling to Avoid Model Collapse on Multi-modal MRI Datasets by Han, Linxuan; Xiao, Sa; Li, Zimeng; Li, Haidong; Zhao, Xiuchao; Han, Yeqing; Guo, Fumin; Zhou, Xin is a scholarly article available to read on EtoBox.

What is Enhanced Masked Image Modeling to Avoid Model Collapse on Multi-modal MRI Datasets about?

Multi-modal magnetic resonance imaging (MRI) provides information of lesions for computer-aided diagnosis from different views. Deep learning algorithms are suitable for identifying specific anatomical structures, segmenting lesions, and classifying diseases. Manual labels are limited due to the high expense, which hinders further improvement of accuracy. Self-supervised learning, particularly masked image modeling (MIM), has shown promise in utilizing unlabeled data. However, we spot model collapse when applying MIM to multi-modal MRI datasets. The performance of downstream tasks does not see any improvement following the collapsed model. To solve model collapse, we analyze and address it in two types: complete collapse and dimensional collapse. We find complete collapse occurs because the collapsed loss value in multi-modal MRI datasets falls below the normally converged loss value. Based on this, the hybrid mask pattern (HMP) masking strategy is introduced to elevate the collapsed loss above the normally converged loss value and avoid complete collapse. Additionally, we reveal that dimensional collapse stems from insufficient feature uniformity in MIM. We mitigate dimensional co

Author

Han, Linxuan; Xiao, Sa; Li, Zimeng; Li, Haidong; Zhao, Xiuchao; Han, Yeqing; Guo, Fumin; Zhou, Xin

Published

2024

Language

EN