Addressing Data Scarcity in 3D Trauma Detection through Self-Supervised and Semi-Supervised Learning with Vertex Relative Position Encoding

TL;DR

This paper introduces a label-efficient method combining self-supervised pre-training and semi-supervised learning to improve 3D trauma detection in CT scans with limited annotated data, achieving significant performance gains.

Contribution

It presents a novel approach that integrates Masked Image Modeling pre-training with semi-supervised detection, significantly reducing the need for annotated data in 3D medical imaging.

Findings

Semi-supervised training improves detection mAP by 115% over supervised-only.

Pre-trained encoder enables high accuracy in injury classification with minimal labeled data.

Method effectively addresses data scarcity in 3D medical image analysis.

Abstract

Accurate detection and localization of traumatic injuries in abdominal CT scans remains a critical challenge in emergency radiology, primarily due to severe scarcity of annotated medical data. This paper presents a label-efficient approach combining self-supervised pre-training with semi-supervised detection for 3D medical image analysis. We employ patch-based Masked Image Modeling (MIM) to pre-train a 3D U-Net encoder on 1,206 CT volumes without annotations, learning robust anatomical representations. The pretrained encoder enables two downstream clinical tasks: 3D injury detection using VDETR with Vertex Relative Position Encoding, and multi-label injury classification. For detection, semi-supervised learning with 2,000 unlabeled volumes and consistency regularization achieves 56.57% validation mAP@0.50 and 45.30% test mAP@0.50 with only 144 labeled training samples, representing a 115% improvement over supervised-only training. For classification, expanding to 2,244 labeled samples yields 94.07% test accuracy across seven injury categories using only a frozen encoder, demonstrating immediately transferable self-supervised features. Our results validate that self-supervised pre-training combined with semi-supervised learning effectively addresses label scarcity in medical imaging, enabling robust 3D object detection with limited annotations.