1 research outputs found
DT-Net: A novel network based on multi-directional integrated convolution and threshold convolution
Since medical image data sets contain few samples and singular features,
lesions are viewed as highly similar to other tissues. The traditional neural
network has a limited ability to learn features. Even if a host of feature maps
is expanded to obtain more semantic information, the accuracy of segmenting the
final medical image is slightly improved, and the features are excessively
redundant. To solve the above problems, in this paper, we propose a novel
end-to-end semantic segmentation algorithm, DT-Net, and use two new convolution
strategies to better achieve end-to-end semantic segmentation of medical
images. 1. In the feature mining and feature fusion stage, we construct a
multi-directional integrated convolution (MDIC). The core idea is to use the
multi-scale convolution to enhance the local multi-directional feature maps to
generate enhanced feature maps and to mine the generated features that contain
more semantics without increasing the number of feature maps. 2. We also aim to
further excavate and retain more meaningful deep features reduce a host of
noise features in the training process. Therefore, we propose a convolution
thresholding strategy. The central idea is to set a threshold to eliminate a
large number of redundant features and reduce computational complexity. Through
the two strategies proposed above, the algorithm proposed in this paper
produces state-of-the-art results on two public medical image datasets. We
prove in detail that our proposed strategy plays an important role in feature
mining and eliminating redundant features. Compared with the existing semantic
segmentation algorithms, our proposed algorithm has better robustness