From Volumes to Slices: Computationally Efficient Contrastive Learning for Sequential Abdominal CT Analysis

Po-Kai Chiu; Hung-Hsuan Chen

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arXiv · 2601.14593

From Volumes to Slices: Computationally Efficient Contrastive Learning for Sequential Abdominal CT Analysis

Po-Kai Chiu·Hung-Hsuan Chen

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Abstract

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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Po-Kai Chiu, Hung-Hsuan Chen. 2026-01-21. From Volumes to Slices: Computationally Efficient Contrastive Learning for Sequential Abdominal CT Analysis. https://arxiv.org/abs/2601.14593

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