The Missing Piece: A Case for Pre-Training in 3D Medical Object Detection

• URL: http://arxiv.org/abs/2509.15947v1
• Date: Fri, 19 Sep 2025 12:55:51 GMT
• Title: The Missing Piece: A Case for Pre-Training in 3D Medical Object Detection
• Authors: Katharina Eckstein, Constantin Ulrich, Michael Baumgartner, Jessica Kächele, Dimitrios Bounias, Tassilo Wald, Ralf Floca, Klaus H. Maier-Hein,
• Abstract summary: Large-scale pre-training holds the promise to advance 3D medical object detection.<n>Existing pre-training approaches for 3D object detection rely on 2D medical data or natural image pre-training.<n>We present the first systematic study of how existing pre-training methods can be integrated into state-of-the-art detection architectures.
• Score: 7.065674915708414
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large-scale pre-training holds the promise to advance 3D medical object detection, a crucial component of accurate computer-aided diagnosis. Yet, it remains underexplored compared to segmentation, where pre-training has already demonstrated significant benefits. Existing pre-training approaches for 3D object detection rely on 2D medical data or natural image pre-training, failing to fully leverage 3D volumetric information. In this work, we present the first systematic study of how existing pre-training methods can be integrated into state-of-the-art detection architectures, covering both CNNs and Transformers. Our results show that pre-training consistently improves detection performance across various tasks and datasets. Notably, reconstruction-based self-supervised pre-training outperforms supervised pre-training, while contrastive pre-training provides no clear benefit for 3D medical object detection. Our code is publicly available at: https://github.com/MIC-DKFZ/nnDetection-finetuning.

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