从体积到切片：用于序列腹部CT分析的计算高效对比学习
From Volumes to Slices: Computationally Efficient Contrastive Learning for Sequential Abdominal CT Analysis
AI总结：
2D-VoCo通过高效对比学习提升腹部CT多器官损伤分类性能，减少对标注数据的依赖。
摘要：
对专家标注的需求限制了深度学习在医学图像分析中的有效性。尽管像体积对比学习（VoCo）这样的3D自监督方法强大且在一定程度上缓解了标注稀缺问题，但其高计算成本和内存消耗成为障碍。我们提出了2D-VoCo，这是VoCo框架在切片级别自监督预训练中的高效适应方法，通过对比学习从未标记的2D CT切片中学习空间-语义特征。预训练的CNN主干网络随后被整合到CNN-LSTM架构中以分类多器官损伤。在RSNA 2023腹部创伤数据集中，2D-VoCo预训练在mAP、精度、召回率和RSNA分数上均优于从头训练。我们的框架提供了一种实用方法，以减少对标注数据的依赖并增强临床CT分析中的模型性能。我们发布了代码以确保可重复性。https://github.com/tkz05/2D-VoCo-CT-Classifier
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
