用多任务残差网络提升肺结节检测分类精度
A Residual Multi-task Network for Joint Classification and Regression in Medical Imaging
- 共享特征层+残差连接，同时处理分类与回归任务
- 模型在肺结节检测中准确率显著提升，抗干扰能力更强
- 适合临床与远程医疗场景下的肺结节智能分析
肺结节的检测与分类在医学图像分析中面临挑战，因其形态和大小多样且隐蔽性强。尽管传统深度学习方法在图像分类上取得成功，但深度网络仍难以精确捕捉肺结节的细微变化。为此，我们提出一种残差多任务网络（Res-MTNet），结合多任务学习与残差学习，通过共享特征提取层并引入残差连接，增强特征表示能力。多任务学习使模型可同时处理多个任务，残差模块有效缓解梯度消失问题，保障深层网络稳定训练，并促进任务间信息共享。Res-MTNet提升了模型的鲁棒性与准确性，为临床医学与远程医疗提供更可靠的肺结节分析工具。
原文摘要 · Abstract (English)
Detection and classification of pulmonary nodules is a challenge in medical image analysis due to the variety of shapes and sizes of nodules and their high concealment. Despite the success of traditional deep learning methods in image classification, deep networks still struggle to perfectly capture subtle changes in lung nodule detection. Therefore, we propose a residual multi-task network (Res-MTNet) model, which combines multi-task learning and residual learning, and improves feature representation ability by sharing feature extraction layer and introducing residual connections. Multi-task learning enables the model to handle multiple tasks simultaneously, while the residual module solves the problem of disappearing gradients, ensuring stable training of deeper networks and facilitating information sharing between tasks. Res-MTNet enhances the robustness and accuracy of the model, providing a more reliable lung nodule analysis tool for clinical medicine and telemedicine.
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