用2D切片对比学习提升腹部CT损伤分析效率
From Volumes to Slices: Computationally Efficient Contrastive Learning for Sequential Abdominal CT Analysis
- 将3D体积对比学习转为2D切片级，降低计算开销
- 在RSNA数据集上，预训练后mAP等指标显著提升
- 适合医疗影像少样本场景下的模型高效训练
专家标注的稀缺限制了深度学习在医学图像分析中的应用。尽管3D自监督方法如体积对比学习（VoCo）能部分缓解标注不足问题，但其高计算成本和内存消耗仍是障碍。本文提出2D-VoCo，一种面向切片级自监督预训练的VoCo框架高效适配版本，通过对比学习从无标注的2D CT切片中学习空间-语义特征。预训练的CNN主干网络被集成到CNN-LSTM架构中，用于多器官损伤分类。在RSNA 2023腹部创伤数据集上，2D-VoCo预训练显著提升了mAP、精确率、召回率和RSNA评分，优于从零开始训练。该框架为减少对标注数据依赖、提升临床CT分析性能提供了实用方案。代码已开源。
原文摘要 · Abstract (English)
The requirement for expert annotations limits the effectiveness of deep learning for medical image analysis. Although 3D self-supervised methods like volume contrast learning (VoCo) are powerful and partially address the labeling scarcity issue, their high computational cost and memory consumption are barriers. We propose 2D-VoCo, an efficient adaptation of the VoCo framework for slice-level self-supervised pre-training that learns spatial-semantic features from unlabeled 2D CT slices via contrastive learning. The pre-trained CNN backbone is then integrated into a CNN-LSTM architecture to classify multi-organ injuries. In the RSNA 2023 Abdominal Trauma dataset, 2D-VoCo pre-training significantly improves mAP, precision, recall, and RSNA score over training from scratch. Our framework provides a practical method to reduce the dependency on labeled data and enhance model performance in clinical CT analysis. We release the code for reproducibility. https://github.com/tkz05/2D-VoCo-CT-Classifier
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