SEGMENTATION MODEL FOR DETECTION OF LUNG CANCER NODULE

In this paper, a 2D segmentation network for lung nodule segmentation is proposed, it uses U-Net as the backbone network, and improves the gradient disappearance and gradient explosion by replacing the convolutional blocks of the original U-Net with residual blocks, the bottleneck with a combination of spatial attention and adaptive ASPP, further the regular up-sampling is replaced with a hybrid up-sampling method, the convolutions in residual unit are done by depth-wise convolution which maps shallow features to deep layers. The attention mechanism is used to reshape the high-level features extracted by the encoder to enhance the global modeling capability of the network. In future work, we will explore new network architectures to improve the robustness and generalization of different CT image datasets.

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