SOURCE-LINKED INTELLIGENCE

A Shared-Backbone Approach for Multi-Task MedMNIST Classification

arXiv · AI, language, vision and robotics · article · Sep 6, 2026 · UTC

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

Read original source ↗ Open in workspace

recordType

paper

region

Global