共享主干网络提升多任务医学图像分类，性能达领先水平。
A Shared-Backbone Approach for Multi-Task MedMNIST Classification
- 采用共享主干+任务专用线性头，统一处理11种异构医学数据集。
- 优化后在11个任务上平均宏F1达0.73294，排名竞赛第六。
- 解决数据分辨率差异问题，适配医学研究与多任务学习场景。
多任务生物医学分类需模型在不同模态和类别分布间实现泛化。本文研究11个异构的MedMNIST数据集，使用各任务宏F1的调和平均值作为评估指标。对比三种主干网络与任务专属线性头的组合，发现MedMNIST API与评估环境存在分辨率域偏移。通过修正该不一致并优化架构特定正则化策略，显著提升性能。最佳配置为ConvNeXt-Tiny主干配合标签平滑，在Tensor Reloaded: Multi-Task MedMNIST竞赛中取得0.73294的调和均值宏F1，位列官方阶段第六。代码已公开于GitHub。
原文摘要 · Abstract (English)
Multi-task biomedical classification requires models to generalize across disparate modalities and class distributions. We study 11 heterogeneous MedMNIST datasets using the harmonic mean of per-task macro-F1. We evaluate three backbones with task-specific linear heads. We identify a resolution domain shift between the MedMNIST API and evaluation environment. Resolving this inconsistency and optimizing architecture-specific regularization substantially improved performance. Our best configuration, a ConvNeXt-Tiny backbone with label smoothing, achieved a leaderboard harmonic-mean macro-F1 of 0.73294 in the Tensor Reloaded: Multi-Task MedMNIST competition, ranking sixth at the close of the official competition phase. Our implementation is publicly available at: https://github.com/GavrilStefan-Dorian/A-Shared-Backbone-Approach-for-Multi-Task-MedMNIST-Classification
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。