Tumor segmentation in automated whole breast ultrasound using bidirectional LSTM neural network and attention mechanism

Ultrasonics ◽  
2021 ◽  
Vol 110 ◽  
pp. 106271
Author(s):  
Pan Pan ◽  
Houjin Chen ◽  
Yanfeng Li ◽  
Naxin Cai ◽  
Lin Cheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Breast Ultrasound ◽  
Attention Mechanism ◽  
Tumor Segmentation ◽  
Bidirectional Lstm

scholarly journals A Novel Bidirectional LSTM and Attention Mechanism based Neural Network for Answer Selection in Community Question Answering

2020 ◽  
Vol 62 (3) ◽  
pp. 1273-1288
Author(s):  
Zhang Bo ◽  
Wang Haowen ◽  
Jiang Longquan ◽  
Yuan Shuhan ◽  
Li Meizi
Keyword(s):  
Neural Network ◽  
Question Answering ◽  
Attention Mechanism ◽  
Community Question Answering ◽  
Bidirectional Lstm

scholarly journals Adaptive Attention Convolutional Neural Network for Liver Tumor Segmentation

2021 ◽  
Vol 11 ◽  
Author(s):  
Shunyao Luan ◽  
Xudong Xue ◽  
Yi Ding ◽  
Wei Wei ◽  
Benpeng Zhu
Keyword(s):  
Neural Network ◽  
Liver Tumor ◽  
Semantic Information ◽  
Liver Tumors ◽  
Ct Images ◽  
Attention Mechanism ◽  
Tumor Segmentation ◽  
Expansion Path ◽  
Liver Tumor Segmentation ◽  
End To End

PurposeAccurate segmentation of liver and liver tumors is critical for radiotherapy. Liver tumor segmentation, however, remains a difficult and relevant problem in the field of medical image processing because of the various factors like complex and variable location, size, and shape of liver tumors, low contrast between tumors and normal tissues, and blurred or difficult-to-define lesion boundaries. In this paper, we proposed a neural network (S-Net) that can incorporate attention mechanisms to end-to-end segmentation of liver tumors from CT images.MethodsFirst, this study adopted a classical coding-decoding structure to realize end-to-end segmentation. Next, we introduced an attention mechanism between the contraction path and the expansion path so that the network could encode a longer range of semantic information in the local features and find the corresponding relationship between different channels. Then, we introduced long-hop connections between the layers of the contraction path and the expansion path, so that the semantic information extracted in both paths could be fused. Finally, the application of closed operation was used to dissipate the narrow interruptions and long, thin divide. This eliminated small cavities and produced a noise reduction effect.ResultsIn this paper, we used the MICCAI 2017 liver tumor segmentation (LiTS) challenge dataset, 3DIRCADb dataset and doctors’ manual contours of Hubei Cancer Hospital dataset to test the network architecture. We calculated the Dice Global (DG) score, Dice per Case (DC) score, volumetric overlap error (VOE), average symmetric surface distance (ASSD), and root mean square error (RMSE) to evaluate the accuracy of the architecture for liver tumor segmentation. The segmentation DG for tumor was found to be 0.7555, DC was 0.613, VOE was 0.413, ASSD was 1.186 and RMSE was 1.804. For a small tumor, DG was 0.3246 and DC was 0.3082. For a large tumor, DG was 0.7819 and DC was 0.7632.ConclusionS-Net obtained more semantic information with the introduction of an attention mechanism and long jump connection. Experimental results showed that this method effectively improved the effect of tumor recognition in CT images and could be applied to assist doctors in clinical treatment.

scholarly journals Colorectal Tumor Segmentation of CT Scans Based on a Convolutional Neural Network With an Attention Mechanism

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 64131-64138 ◽  
Author(s):  
Yun Pei ◽  
Lin Mu ◽  
Yu Fu ◽  
Kan He ◽  
Hong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Colorectal Tumor ◽  
Attention Mechanism ◽  
Ct Scans ◽  
Tumor Segmentation
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