Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning

• URL: http://arxiv.org/abs/2406.08634v1
• Date: Wed, 12 Jun 2024 20:35:16 GMT
• Title: Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning
• Authors: Zhongao Sun, Jiameng Li, Yuhan Wang, Jiarong Cheng, Qing Zhou, Chun Li,
• Abstract summary: Brain tumor segmentation remains a significant challenge, particularly in the context of multi-modal magnetic resonance imaging (MRI) We propose a novel strategy, which is called masked predicted pre-training, enabling robust feature learning from incomplete modality data. In the fine-tuning phase, we utilize a knowledge distillation technique to align features between complete and missing modality data, simultaneously enhancing model robustness.
• Score: 6.44069573245889
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain tumor segmentation remains a significant challenge, particularly in the context of multi-modal magnetic resonance imaging (MRI) where missing modality images are common in clinical settings, leading to reduced segmentation accuracy. To address this issue, we propose a novel strategy, which is called masked predicted pre-training, enabling robust feature learning from incomplete modality data. Additionally, in the fine-tuning phase, we utilize a knowledge distillation technique to align features between complete and missing modality data, simultaneously enhancing model robustness. Notably, we leverage the Holder pseudo-divergence instead of the KLD for distillation loss, offering improve mathematical interpretability and properties. Extensive experiments on the BRATS2018 and BRATS2020 datasets demonstrate significant performance enhancements compared to existing state-of-the-art methods.

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