Error-free Training for MedMNIST Datasets

arXiv cs.AI / 4/22/2026

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Key Points

The paper proposes a new training concept called “Artificial Special Intelligence” aimed at enabling classification models to learn without repeated errors.
The proposed method is evaluated on 18 MedMNIST biomedical image datasets, demonstrating near-perfect training performance.
The authors attribute performance issues in three datasets to a “double-labeling” problem, suggesting data labeling quality limits results.
Overall, the work argues that careful handling of dataset labeling can help models reach error-free training on complex biomedical classification tasks.

Abstract

In this paper, we introduce a new concept called Artificial Special Intelligence by which Machine Learning models for the classification problem can be trained error-free, thus acquiring the capability of not making repeated mistakes. The method is applied to 18 MedMNIST biomedical datasets. Except for three datasets, which suffer from the double-labeling problem, all are trained to perfection.