Efficient 2D CT Foundation Model for Contrast Phase Classification

• URL: http://arxiv.org/abs/2501.14066v1
• Date: Thu, 23 Jan 2025 20:01:33 GMT
• Title: Efficient 2D CT Foundation Model for Contrast Phase Classification
• Authors: Benjamin Hou, Tejas Sudharshan Mathai, Pritam Mukherjee, Xinya Wang, Ronald M. Summers, Zhiyong Lub,
• Abstract summary: A 2D foundation model was trained to generate embeddings from 2D CT slices for downstream contrast phase classification. The model was validated on the VinDr Multiphase dataset and externally validated on the WAW-TACE dataset. Compared to 3D supervised models, the approach trained faster, performed as well or better, and showed greater robustness to domain shifts.
• Score: 4.650290073034678
• License:
• Abstract: Purpose: The purpose of this study is to harness the efficiency of a 2D foundation model to develop a robust phase classifier that is resilient to domain shifts. Materials and Methods: This retrospective study utilized three public datasets from separate institutions. A 2D foundation model was trained on the DeepLesion dataset (mean age: 51.2, s.d.: 17.6; 2398 males) to generate embeddings from 2D CT slices for downstream contrast phase classification. The classifier was trained on the VinDr Multiphase dataset and externally validated on the WAW-TACE dataset. The 2D model was also compared to three 3D supervised models. Results: On the VinDr dataset (146 male, 63 female, 56 unidentified), the model achieved near-perfect AUROC scores and F1 scores of 99.2%, 94.2%, and 93.1% for non-contrast, arterial, and venous phases, respectively. The `Other' category scored lower (F1: 73.4%) due to combining multiple contrast phases into one class. On the WAW-TACE dataset (mean age: 66.1, s.d.: 10.0; 185 males), the model showed strong performance with AUROCs of 91.0% and 85.6%, and F1 scores of 87.3% and 74.1% for non-contrast and arterial phases. Venous phase performance was lower, with AUROC and F1 scores of 81.7% and 70.2% respectively, due to label mismatches. Compared to 3D supervised models, the approach trained faster, performed as well or better, and showed greater robustness to domain shifts. Conclusion: The robustness of the 2D Foundation model may be potentially useful for automation of hanging protocols and data orchestration for clinical deployment of AI algorithms.

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