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AI models improve Pap smear classification trustworthiness

By PulseAugur Editorial · [1 sources] ·

Researchers have explored selective prediction and uncertainty-aware referral for Pap smear classification using deep learning models. By fine-tuning Swin-Tiny and TinyViT-5M transformers on the Herlev Pap smear dataset, they found that an ensemble of these models significantly reduced the area under the risk-coverage curve, indicating improved ability to rank predictions by trustworthiness. While the ensemble improved the risk-coverage tradeoff, it also resulted in worse absolute calibration and a higher number of false negatives compared to individual models. AI

IMPACT This research could lead to more reliable AI-assisted diagnostic tools by improving how model uncertainty is handled in critical applications.

RANK_REASON Academic paper detailing a novel approach to model evaluation and performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI models improve Pap smear classification trustworthiness

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14 / 100
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Academic paper detailing a novel approach to model evaluation and performance. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Nisreen Albzour, Sarah S. Lam ·

    Selective Prediction and Uncertainty-Aware Referral for Pap Smear Classification

    arXiv:2609.17545v1 Announce Type: new Abstract: Deep learning models for cervical cytology are almost always evaluated as if every prediction must be acted upon, yet a screening system deployed alongside a cytopathologist need not classify every slide: it can defer the cases it i…

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