The pancreas is located deep in the abdominal cavity, and its structure and adjacent relationship are complex. It is very difficult to treat it accurately. In order to solve the problem of automatic segmentation of pancreatic tissue in CT images, we apply the multi-scale idea of convolution neural network to Transformer, and propose a Multi-Scale Swin Transformer and Complementary Self-Attention Fusion Network for Pancreas Segmentation. Specifically, the multi-scale Swin Transformer module constructs different receptive fields through different window sizes to obtain multi-scale information; the different features of the encoder and decoder are effectively fused through a complementary self-attention fusion module. By comparing experimental evaluations on the NIH-TCIA dataset, our method improves Dice, sensitivity, and IOU by 3.9%, 6.4%, and 5.3% respectively compared to the baseline, which outperforms current state-of-the-art medical image segmentation methods.
The traditional Fuzzy C-means (FCM) algorithm is stable and easy to be implemented. However, the data elements in the cluster boundary of FCM are easily clustered into incorrect classes making the efficiency of FCM algorithm reduced. Aiming at solving this problem, this paper presents a Rough-FCM algorithm which is combined FCM algorithm with rough set according to new equations. We take the advantage of the positive region set and the boundary region set of rough set. First, Rough-FCM algorithm divides the data elements into the positive region set or the boundary region set of all classes according to the threshold we set. Second, it updates the cluster centers and membership matrixes with new equations. Thus, we can execute the second clustering based on first clustering of FCM. By comparing the experimental results of the Rough-FCM with K-means, DBSCAN and FCM according to four clustering evaluation indexes on both synthetic and real datasets, we evaluate our proposed algorithm and improve outcomes from most of datasets by adopting these three classic clustering algorithms mentioned above.
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