biomedical-classification

This paper introduces TIER-MoE, a risk-guided subspace mixture-of-experts model for multimodal biomedical classification that estimates sample-specific modality reliability from out-of-fold predictions and routes modalities to experts, improving performance and calibration on four public datasets.

本文系统评估了五种不平衡处理方法（RUS、ROS、SMOTE、重加权、直接F1优化）在三个生物医学数据集（表格、文本、图像）上使用不同复杂度模型的效果。结果表明，收益取决于模型复杂度和数据模态，其中ROS、重加权和直接F1优化对非结构化数据上的复杂模型有效。