用本地数据进行培训对深度学习至关重要 磁力共振成像 前列腺癌检测
Shawn G Carere1,2, John Jewell2, Paola V Nasute Fauerbach1
1Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
概括
局部MRI数据显著提高了前列腺癌细分的AI性能,优于在较大的外部数据集上训练的模型. 这凸显了本地数据在克服领域转移挑战方面的关键作用.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 域名转移会对医疗成像中的AI模型性能产生负面影响.
- 关于MRI前列腺癌细分领域转移的先前研究使用了有限的队列.
- 大规模的外部数据集可能无法完全捕捉本地数据特征.
研究的目的:
- 评估在本地MRI数据上训练的AI模型是否优于在前列腺癌细分方面的外部数据上训练的模型.
- 评估队列大小 (>1000次考试) 对域转移效应的影响.
- 确定对于强大的细分模型的本地数据的必要性.
主要方法:
- 使用公共 (PICAI) 和当地MRI数据集模拟了一个多机构的财团.
- 训练 nnUNet-v2 模型在组合 (中央-火车),外部 (PICAI-火车) 和本地 (地方-火车) 数据上.
- 在本地测试集上使用PICAI评分评估模型准确性,并通过引导测试来测试显著性.
主要成果:
- 在本地培训数据中,只有很小的一小部分 (22%) 与外部培训绩效相匹配.
- 在综合或局部数据 (PICAI评分 [95% CI] 65 [58-71]和66 [60-72]) 上训练的模型表现优于仅外部模型 (58 [51-64],P < .002).
- 减少培训组的大小并没有改变这些绩效趋势.
结论:
- 域移动显著限制了MRI前列腺癌细分性能,即使有超过1000个外部检查.
- 当地MRI数据对于在规模化前列腺癌细分模型中实现最佳性能至关重要.
- 整合本地数据对于开发可靠的AI工具在这个领域至关重要.
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