缺模态也能准分割脑肿瘤，靠的是信息与差异量化。
Robust Brain Tumor Segmentation with Incomplete MRI Modalities Using Hölder Divergence and Mutual Information-Enhanced Knowledge Transfer
- 用霍尔德散度和互信息损失，动态调整网络适应缺失模态。
- 在BraTS 2018/2020上，缺模态时分割精度优于现有方法。
- 适合临床中模态不全的脑肿瘤自动分割场景。
多模态MRI为精准脑肿瘤分割提供关键互补信息，但因图像质量、协议差异、患者过敏或费用限制，常出现某些模态缺失。为此，我们提出一种鲁棒的单模态并行处理框架，在模态不全时仍可实现高精度分割。该模型利用霍尔德散度和互信息，保留模态特异性特征，同时根据可用输入动态调整网络参数。通过基于差异与信息的损失函数，有效量化预测与真实标签间的偏差，确保分割结果稳定准确。在BraTS 2018和BraTS 2020数据集上的大量实验表明，该方法在处理缺失模态时表现优于现有技术。
原文摘要 · Abstract (English)
Multimodal MRI provides critical complementary information for accurate brain tumor segmentation. However, conventional methods struggle when certain modalities are missing due to issues such as image quality, protocol inconsistencies, patient allergies, or financial constraints. To address this, we propose a robust single-modality parallel processing framework that achieves high segmentation accuracy even with incomplete modalities. Leveraging Holder divergence and mutual information, our model maintains modality-specific features while dynamically adjusting network parameters based on the available inputs. By using these divergence- and information-based loss functions, the framework effectively quantifies discrepancies between predictions and ground-truth labels, resulting in consistently accurate segmentation. Extensive evaluations on the BraTS 2018 and BraTS 2020 datasets demonstrate superior performance over existing methods in handling missing modalities.
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