Li Hengfan, Xu Xuanbo, Liu Ziheng, Xia Qingfeng, Xia Min
Jiangsu Collaborative Innovation Center for Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Institute of Systems Science, National University of Singapore, Singapore 119077, Singapore.
Sensors (Basel). 2024 Dec 5;24(23):7799. doi: 10.3390/s24237799.
Traditional medical image sensors face multiple challenges. First, these sensors typically rely on large amounts of labeled data, which are time-consuming and costly to obtain. Second, when the data volume and image size are large, traditional sensors have limited computational power, making it difficult to effectively train and infer models. Additionally, traditional sensors have poor generalization ability and struggle to adapt to datasets with different modalities. This paper devises a novel framework, named LSDSL, and deploys it in the sensor. LSDSL utilizes low-quality sensor data for semi-supervised learning in medical image segmentation. in supervised learning, we devise the hard region exploration (hre) module to enhance the model's comprehension of low-quality pixels in hard regions. in unsupervised learning, we introduce a pseudo-label sharing (ps) module, which allows low-quality pixels in one network to learn from the high-quality pixels in the other networks. our model outperforms other semi-supervised methods on the datasets of two different modalities (CT and MRI) in medical image sensors, achieving superior inference speed and segmentation accuracy.
传统医学图像传感器面临多重挑战。首先，这些传感器通常依赖大量有标签数据，获取这些数据既耗时又昂贵。其次，当数据量和图像尺寸较大时，传统传感器的计算能力有限，难以有效地训练和推断模型。此外，传统传感器的泛化能力较差，难以适应不同模态的数据集。本文设计了一种名为LSDSL的新型框架，并将其部署在传感器中。LSDSL利用低质量传感器数据进行医学图像分割的半监督学习。在监督学习中，我们设计了硬区域探索（hre）模块，以增强模型对硬区域中低质量像素的理解。在无监督学习中，我们引入了伪标签共享（ps）模块，该模块允许一个网络中的低质量像素从其他网络中的高质量像素学习。我们的模型在医学图像传感器中两种不同模态（CT和MRI）的数据集上优于其他半监督方法，实现了卓越的推理速度和分割精度。
