scholarly journals AM-AN: Adversarial Network Based on Attention Mechanism for Medical Image Segmentation

2020 ◽  
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Attention Mechanism ◽  
Adversarial Network

scholarly journals Explainable Medical Image Segmentation via Generative Adversarial Networks and Layer-wise Relevance Propagation

2021 ◽  
Vol 1 (1) ◽  
pp. 20-22
Author(s):  
Awadelrahman M. A. Ahmed ◽  
Leen A. M. Ali
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Input Image ◽  
Medical Image Segmentation ◽  
Jaccard Index ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

This paper contributes in automating medical image segmentation by proposing generative adversarial network based models to segment both polyps and instruments in endoscopy images. A main contribution of this paper is providing explanations for the predictions using layer-wise relevance propagation approach, showing which pixels in the input image are more relevant to the predictions. The models achieved 0.46 and 0.70, on Jaccard index and 0.84 and 0.96 accuracy, on the polyp segmentation and the instrument segmentation, respectively.

Multi-Attention Mechanism Medical Image Segmentation Combined with Word Embedding Technology

2020 ◽  
Vol 54 (6) ◽  
pp. 560-571
Author(s):  
Junlong Cheng ◽  
Shengwei Tian ◽  
Long Yu ◽  
Hongfeng You
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Attention Mechanism ◽  
Word Embedding

scholarly journals Optic Disc Segmentation Using Attention-Based U-Net and the Improved Cross-Entropy Convolutional Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 844
Author(s):  
Baixin Jin ◽  
Pingping Liu ◽  
Peng Wang ◽  
Lida Shi ◽  
Jing Zhao
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
Optic Disc ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Medical Image Segmentation ◽  
Attention Mechanism ◽  
Cross Entropy ◽  
Segmentation Task

Medical image segmentation is an important part of medical image analysis. With the rapid development of convolutional neural networks in image processing, deep learning methods have achieved great success in the field of medical image processing. Deep learning is also used in the field of auxiliary diagnosis of glaucoma, and the effective segmentation of the optic disc area plays an important assistant role in the diagnosis of doctors in the clinical diagnosis of glaucoma. Previously, many U-Net-based optic disc segmentation methods have been proposed. However, the channel dependence of different levels of features is ignored. The performance of fundus image segmentation in small areas is not satisfactory. In this paper, we propose a new aggregation channel attention network to make full use of the influence of context information on semantic segmentation. Different from the existing attention mechanism, we exploit channel dependencies and integrate information of different scales into the attention mechanism. At the same time, we improved the basic classification framework based on cross entropy, combined the dice coefficient and cross entropy, and balanced the contribution of dice coefficients and cross entropy loss to the segmentation task, which enhanced the performance of the network in small area segmentation. The network retains more image features, restores the significant features more accurately, and further improves the segmentation performance of medical images. We apply it to the fundus optic disc segmentation task. We demonstrate the segmentation performance of the model on the Messidor dataset and the RIM-ONE dataset, and evaluate the proposed architecture. Experimental results show that our network architecture improves the prediction performance of the base architectures under different datasets while maintaining the computational efficiency. The results render that the proposed technologies improve the segmentation with 0.0469 overlapping error on Messidor.

U-Net Medical Image Segmentation Based on Attention Mechanism Combination

2021 ◽  
pp. 805-813
Author(s):  
Tao Liu ◽  
Beibei Qian ◽  
Ya Wang ◽  
Qunli Xie
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Attention Mechanism

scholarly journals SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation

2018 ◽  
Vol 16 (3-4) ◽  
pp. 383-392 ◽  
Author(s):  
Yuan Xue ◽  
Tao Xu ◽  
Han Zhang ◽  
L. Rodney Long ◽  
Xiaolei Huang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Multi Scale ◽  
Adversarial Network

scholarly journals AF-Net: A Medical Image Segmentation Network Based on Attention Mechanism and Feature Fusion

2021 ◽  
Vol 69 (2) ◽  
pp. 1877-1891
Author(s):  
Guimin Hou ◽  
Jiaohua Qin ◽  
Xuyu Xiang ◽  
Yun Tan ◽  
Neal N. Xiong
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Feature Fusion ◽  
Medical Image Segmentation ◽  
Attention Mechanism

scholarly journals Difficulty-Aware Attention Network with Confidence Learning for Medical Image Segmentation

2019 ◽  
Vol 33 ◽  
pp. 1085-1092 ◽  
Author(s):  
Dong Nie ◽  
Li Wang ◽  
Lei Xiang ◽  
Sihang Zhou ◽  
Ehsan Adeli ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Structural Information ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Adversarial Learning ◽  
Image Guided Radiation Therapy ◽  
Adversarial Network ◽  
Specific Effects ◽  
Therapy And Diagnosis

Medical image segmentation is a key step for various applications, such as image-guided radiation therapy and diagnosis. Recently, deep neural networks provided promising solutions for automatic image segmentation; however, they often perform good on regular samples (i.e., easy-to-segment samples), since the datasets are dominated by easy and regular samples. For medical images, due to huge inter-subject variations or disease-specific effects on subjects, there exist several difficult-to-segment cases that are often overlooked by the previous works. To address this challenge, we propose a difficulty-aware deep segmentation network with confidence learning for end-to-end segmentation. The proposed framework has two main contributions: 1) Besides the segmentation network, we also propose a fully convolutional adversarial network for confidence learning to provide voxel-wise and region-wise confidence information for the segmentation network. We relax the adversarial learning to confidence learning by decreasing the priority of adversarial learning, so that we can avoid the training imbalance between generator and discriminator. 2) We propose a difficulty-aware attention mechanism to properly handle hard samples or hard regions considering structural information, which may go beyond the shortcomings of focal loss. We further propose a fusion module to selectively fuse the concatenated feature maps in encoder-decoder architectures. Experimental results on clinical and challenge datasets show that our proposed network can achieve state-of-the-art segmentation accuracy. Further analysis also indicates that each individual component of our proposed network contributes to the overall performance improvement.

Design of Superpiexl U-Net Network for Medical Image Segmentation

2019 ◽  
Vol 31 (6) ◽  
pp. 1007 ◽  
Author(s):  
Haiou Wang ◽  
Hui Liu ◽  
Qiang Guo ◽  
Kai Deng ◽  
Caiming Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation
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