用少量样本训练模型，自动识别胎盘早剥超声中的出血特征。
An Automatic Detection Method for Hematoma Features in Placental Abruption Ultrasound Images Based on Few-Shot Learning
- 基于小样本学习改进YOLOv11n，融合小波与坐标卷积增强特征提取。
- 检测准确率达78%，较YOLOv11n提升2.5%，较YOLOv8提升13.7%。
- 适合临床辅助诊断场景，尤其在数据少、遮挡多的情况下表现优。
胎盘早剥是妊娠期严重并发症，早期精准诊断对母婴安全至关重要。传统超声诊断高度依赖医生经验，易出现主观偏差和诊断不一致。本文提出一种基于小样本学习的改进模型EH-YOLOv11n（Enhanced Hemorrhage-YOLOv11n），实现胎盘超声图像中血肿特征的自动检测。该模型通过多维度优化：引入小波卷积与坐标卷积，强化频域与空间特征提取；采用级联组注意力机制，抑制超声伪影与遮挡干扰，提升边界框定位精度。实验表明，检测准确率达78%，较YOLOv11n提升2.5%，较YOLOv8提高13.7%。在精确率-召回率曲线、置信度得分及遮挡场景下均表现显著优势。结合高精度与实时处理能力，为胎盘早剥的计算机辅助诊断提供可靠方案，具有重要临床应用价值。
原文摘要 · Abstract (English)
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.
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