融合LSTM与自注意力，提升乳腺超声图像分割精度
UNet++ and LSTM combined approach for Breast Ultrasound Image Segmentation
- 在UNet++中加入LSTM和自注意力，捕捉图像时序特征
- 在BUSI with GT数据集上达到98.88%准确率
- 适合医学影像分割研究者参考
乳腺癌是全球女性主要死因之一，早期检测对降低死亡率至关重要。现有研究利用乳腺超声图像数据集BUSI，采用UNet和UNet++网络已取得良好分割效果。然而，这些模型未充分考虑图像中的时序信息。本文通过在UNet++架构中引入LSTM层与自注意力机制，以挖掘图像的时序特性，并结合多尺度特征提取模块捕捉不同尺度特征。在使用数据增强的BUSI with GT数据集上，本方法实现了98.88%的准确率、99.53%特异性、95.34%精确率、91.20%敏感度、93.74% F1分数和92.74% Dice系数，性能优于现有先进方法。
原文摘要 · Abstract (English)
Breast cancer stands as a prevalent cause of fatality among females on a global scale, with prompt detection playing a pivotal role in diminishing mortality rates. The utilization of ultrasound scans in the BUSI dataset for medical imagery pertaining to breast cancer has exhibited commendable segmentation outcomes through the application of UNet and UNet++ networks. Nevertheless, a notable drawback of these models resides in their inattention towards the temporal aspects embedded within the images. This research endeavors to enrich the UNet++ architecture by integrating LSTM layers and self-attention mechanisms to exploit temporal characteristics for segmentation purposes. Furthermore, the incorporation of a Multiscale Feature Extraction Module aims to grasp varied scale features within the UNet++. Through the amalgamation of our proposed methodology with data augmentation on the BUSI with GT dataset, an accuracy rate of 98.88%, specificity of 99.53%, precision of 95.34%, sensitivity of 91.20%, F1-score of 93.74, and Dice coefficient of 92.74% are achieved. These findings demonstrate competitiveness with cutting-edge techniques outlined in existing literature.
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