English(EN) Selective Prediction and Uncertainty-Aware Referral for Pap Smear Classification

AI模型提高宫颈涂片分类的可信度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员利用深度学习模型探索了用于宫颈涂片分类的选择性预测和不确定性感知转介。通过在Herlev宫颈涂片数据集上微调Swin-Tiny和TinyViT-5M transformer，他们发现这些模型的集成显著降低了风险覆盖曲线下面积，表明其按可信度对预测进行排名的能力有所提高。虽然集成模型改善了风险覆盖权衡，但与单个模型相比，它也导致了更差的绝对校准和更高数量的假阴性。 AI

影响这项研究通过改进模型不确定性在关键应用中的处理方式，可能带来更可靠的AI辅助诊断工具。

排序理由详细介绍模型评估和性能新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 12 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍模型评估和性能新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Nisreen Albzour, Sarah S. Lam · 2026-09-17 04:00

宫颈涂片分类的选择性预测和不确定性感知推荐

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…

本文如何被排名

报道来源 [1]

宫颈涂片分类的选择性预测和不确定性感知推荐

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