English(EN) Is a CNN-Transformer Ensemble Worth It When a Chest X-Ray Foundation Model Is Available?

胸部X光AI：基础模型优于CNN-Transformer集成

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

一项新的研究论文调查了将CNN和Vision Transformer结合用于胸部X光分类的有效性。研究发现，ImageNet预训练模型的集成性能不如在胸部放射线照片上预训练的单一基础模型EVA-X-S。这种集成方法还需要更多的计算资源和参数，这表明专门的基础模型在该任务上可能更有效率和更有效。 AI

影响与通用的集成模型相比，专门的基础模型可能在医学成像任务上提供更好的性能和效率。

排序理由评估AI模型在特定任务上性能的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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arXiv cs.CV TIER_1 English(EN) · Aditya Seelaboyina, Irfan Khan · 2026-11-11 05:00

当有胸部X光基础模型时，CNN-Transformer集成模型是否值得？

arXiv:2611.13014v1 Announce Type: cross Abstract: Ensembles of ImageNet-pretrained convolutional networks and Vision Transformers are a common way to improve multi-label chest X-ray classification. We ask whether such an ensemble is still worth its cost when a model pretrained on…

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当有胸部X光基础模型时，CNN-Transformer集成模型是否值得？

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