用少量标注数据实现心肌影像组织分类与定位，提升医疗诊断效率。
Few-Shot Classification and Anatomical Localization of Tissues in SPECT Imaging
- 基于原型网络实现少样本组织分类，使用预训练ResNet-18
- 分类准确率达93.33%（验证集），定位重建损失为1.395
- 适合标注数据稀缺的医学影像场景，尤其适用于心功能评估
精准的组织分类与解剖定位对医学诊断与研究至关重要，深度学习可有效实现该目标。然而，标注数据有限仍是主要挑战。为此，本文分别采用原型网络（Prototypical Networks）与传播-重建网络（PRNet）解决单光子发射计算机断层成像（SPECT）图像中的少样本分类与定位问题。以心脏区域的2D切片图像为实验对象，原型网络结合预训练的ResNet-18主干，对心室、心肌和肝脏组织分类，在训练集上达到96.67%准确率，验证集达93.33%。PRNet采用编码器-解码器结构并引入跳跃连接，针对2D图像进行适配，训练损失为1.395，能精确重建图像块并捕捉空间关系。结果表明，原型网络在小样本条件下具备优异分类能力，而PRNet可实现高效解剖定位，为深度学习在医学影像中的应用提供新路径。
原文摘要 · Abstract (English)
Accurate classification and anatomical localization are essential for effective medical diagnostics and research, which may be efficiently performed using deep learning techniques. However, availability of limited labeled data poses a significant challenge. To address this, we adapted Prototypical Networks and the Propagation-Reconstruction Network (PRNet) for few-shot classification and localization, respectively, in Single Photon Emission Computed Tomography (SPECT) images. For the proof of concept we used a 2D-sliced image cropped around heart. The Prototypical Network, with a pre-trained ResNet-18 backbone, classified ventricles, myocardium, and liver tissues with 96.67% training and 93.33% validation accuracy. PRNet, adapted for 2D imaging with an encoder-decoder architecture and skip connections, achieved a training loss of 1.395, accurately reconstructing patches and capturing spatial relationships. These results highlight the potential of Prototypical Networks for tissue classification with limited labeled data and PRNet for anatomical landmark localization, paving the way for improved performance in deep learning frameworks.
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