From Volumes to Slices: Computationally Efficient Contrastive Learning for Sequential Abdominal CT Analysis
This work addresses the problem of reducing dependency on labeled data and enhancing model performance for clinical CT analysis, though it is incremental as it adapts an existing framework.
The paper tackles the high computational cost and memory consumption of 3D self-supervised methods for medical image analysis by proposing 2D-VoCo, an efficient slice-level adaptation, which significantly improves mAP, precision, recall, and RSNA score over training from scratch on the RSNA 2023 Abdominal Trauma dataset.
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