A Shared-Backbone Approach for Multi-Task MedMNIST Classification

2026-09-06

AI Summary

研究通过共享骨干网络和任务特定线性头解决多任务MedMNIST分类中的模态与类别分布差异问题，最佳模型使用ConvNeXt-Tiny和标签平滑技术。

Abstract

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

Problem

• Multi-task biomedical classification
• MedMNIST
• resolution domain shift

Innovation

• Shared-Backbone
• Resolution Domain Shift
• Architecture-Specific Regularization
• ConvNeXt-Tiny
• Label Smoothing