An Automatic Detection Method for Hematoma Features in Placental Abruption Ultrasound Images Based on Few-Shot Learning

2025-10-24

AI Summary

Early diagnosis of placental abruption relies heavily on subjective and inconsistent ultrasound assessments. To address this challenge, we propose EH-YOLOv11n—a lightweight, small-sample–optimized model for accurate hematoma detection in placental ultrasound images. Our method integrates wavelet convolution to enhance frequency-domain feature representation, coordinate convolution to improve spatial localization precision, and a cascaded group attention mechanism to suppress artifacts and occlusion-induced interference; additionally, we refine the bounding box regression strategy. Evaluated under limited annotated data, EH-YOLOv11n achieves a detection accuracy of 78.0%, outperforming baseline YOLOv11n and YOLOv8 by 2.5% and 13.7%, respectively. Notably, it demonstrates markedly improved robustness on occluded and low-confidence samples. These results underscore its potential as a clinical decision-support tool for early placental abruption diagnosis.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Segmentation
• Machine Learning: Hardware-aware ML

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Placental abruption is a severe complication during pregnancy, and its early accurate diagnosis is crucial for ensuring maternal and fetal safety. Traditional ultrasound diagnostic methods heavily rely on physician experience, leading to issues such as subjective bias and diagnostic inconsistencies. This paper proposes an improved model, EH-YOLOv11n (Enhanced Hemorrhage-YOLOv11n), based on small-sample learning, aiming to achieve automatic detection of hematoma features in placental ultrasound images. The model enhances performance through multidimensional optimization: it integrates wavelet convolution and coordinate convolution to strengthen frequency and spatial feature extraction; incorporates a cascaded group attention mechanism to suppress ultrasound artifacts and occlusion interference, thereby improving bounding box localization accuracy. Experimental results demonstrate a detection accuracy of 78%, representing a 2.5% improvement over YOLOv11n and a 13.7% increase over YOLOv8. The model exhibits significant superiority in precision-recall curves, confidence scores, and occlusion scenarios. Combining high accuracy with real-time processing, this model provides a reliable solution for computer-aided diagnosis of placental abruption, holding significant clinical application value.

Problem

• Automating hematoma detection in placental ultrasound images using few-shot learning
• Reducing subjective bias in placental abruption diagnosis through computer-aided detection
• Improving detection accuracy for ultrasound artifacts and occlusion interference

Innovation

• Enhanced YOLOv11n model for hematoma detection
• Wavelet and coordinate convolution for feature extraction
• Cascaded attention mechanism to reduce ultrasound artifacts