Deep learning has become an essential tool in medical image analysis owing to its remarkable performance. Target classification and model interpretability are key applications of deep learning in medical image analysis, and hence many deep learning-based algorithms have emerged. Many existing deep learning-based algorithms include pooling operations, which are a type of subsampling used to enlarge the receptive field. However, pooling operations degrade the image details in terms of signal processing theory, which is significantly sensitive to small objects in an image. Therefore, in this study, we designed a Rense block and edge conservative module to effectively manipulate previous feature information in the feed-forward learning process. Specifically, a Rense block, an optimal design that incorporates skip connections of residual and dense blocks, was demonstrated through mathematical analysis. Furthermore, we avoid blurring of the features in the pooling operation through a compensation path in the edge conservative module. Two independent CT datasets of kidney stones and lung tumors, in which small lesions are often included in the images, were used to verify the proposed RenseNet. The results of the classification and explanation heatmaps show that the proposed RenseNet provides the best inference and interpretation compared to current state-of-the-art methods. The proposed RenseNet can significantly contribute to efficient diagnosis and treatment because it is effective for small lesions that might be misclassified or misinterpreted.
深度学习凭借其卓越性能已成为医学图像分析中不可或缺的工具。目标分类与模型可解释性是深度学习在医学图像分析中的关键应用领域，因此涌现出许多基于深度学习的算法。现有许多基于深度学习的算法包含池化操作，这是一种用于扩大感受野的下采样方法。然而从信号处理理论来看，池化操作会降低图像细节的清晰度，这对图像中的微小病灶极为敏感。为此，本研究设计了Rense模块和边缘保守模块，在前向传播学习过程中有效处理先前的特征信息。具体而言，通过数学分析验证了Rense模块——这是一种融合残差模块与密集模块跳跃连接的最优设计。此外，我们通过边缘保守模块中的补偿路径避免池化操作造成的特征模糊化。采用包含肾脏结石和肺部肿瘤的两个独立CT数据集进行验证，这些图像常包含微小病灶。分类结果与解释性热图显示，相较于当前最先进方法，所提出的RenseNet在推理能力和可解释性方面均表现最优。该网络对易被误分类或误判的微小病灶具有显著检测效果，可为高效诊断与治疗提供重要支持。
肿瘤(癌症)患者之家