ArticleBioengineering (Basel, Switzerland)2026
Beyond Size and Class Balance: Alpha as a New Dataset Quality Metric for Deep Learning.
Article in Bioengineering (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 citing papers in PubMed.
- Efficient material selection for training occluded mmWave radar-based gesture recognisers.Scientific reports · 2026Article
- Generative deep learning for foundational video translation in ultrasound.Scientific reports · 2026Article
- Which Similarity-Sensitive Entropy (Sentropy)?ArXiv · 2026Article
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Abstract
In deep-learning-based image classification, achieving high performance requires diverse training sets. However, the current best practice-maximizing dataset size and class balance-does not guarantee dataset diversity. We hypothesized that, for a given model architecture, performance improves by maximizing diversity more directly. To test this hypothesis, we introduce a comprehensive framework of "sentropic" diversity measures or "sentropies" from ecology that generalizes familiar quantities like Shannon entropy by accounting for similarities among images. Size and class balance emerge as special cases. Analyzing thousands of subsets from seven medical-imaging datasets showed that the best correlates of performance were not size or class balance but
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