ArticleImmunologic research2026
Deep learning for automated classification of antinuclear antibody patterns on HEp-2 indirect immunofluorescence images.
Article in Immunologic research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Standardizing the interpretation of ANA patterns is a persistent challenge in rheumatology due to inter-observer discordance. This research introduces an ensemble deep learning framework optimized for the International Consensus on ANA Patterns (ICAP) system. By integrating ResNet-50 and EfficientNet-B0 via validation-tuned weighting and logit-level averaging, we processed both a single-label benchmark and a high-variability clinical dataset containing overlapping (multi-label) patterns. Our model demonstrated high fidelity in the single-label setting (92.5% accuracy; MCC = 0.9193). Critically, in the independent hospital cohort, the system managed the complexities of co-occurring patterns with a 1.26% Hamming loss and a micro F1-score of 82.91%. By achieving "near-miss" accuracy (within two labels) in over 95% of clinical cases, this framework demonstrates its potential utility as a decision-support tool that may help mitigate the inherent variability associated with manual HEp-2 cell analysis.
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