Channel Attention Module with Multi-scale Grid Average Pooling for Breast Cancer Segmentation in an Ultrasound Image

Lee, Haeyun; Park, Jinhyoung; Hwang, Jae Youn

doi:10.1109/tuffc.2020.2972573

Cited by 86 publications

(46 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2) Average pooling layer: It is a pooling operation that selects the average element from the filter's covered area of the feature map. Average pooling counts all values and passes them on to the next layer, implying that all values are utilized for feature mapping and output generation, which is a comprehensive calculation [20]. Fig.…”

Section: Cnn Classifiermentioning

confidence: 99%

Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification

Tasnim¹,

Chakraborty²,

Shamrat³

et al. 2021

IJACSA

View full text Add to dashboard Cite

In recent years, the area of Medicine and Healthcare has made significant advances with the assistance of computational technology. During this time, new diagnostic techniques were developed. Cancer is the world's second-largest cause of mortality, claiming the lives of one out of every six individuals. The colon cancer variation is the most frequent and lethal of the numerous kinds of cancer. Identifying the illness at an early stage, on the other hand, substantially increases the odds of survival. A cancer diagnosis may be automated by using the power of Artificial Intelligence (AI), allowing us to evaluate more cases in less time and at a lower cost. In this research, CNN models are employed to analyse imaging data of colon cells. For colon cell image classification, CNN with max pooling and average pooling layers and MobileNetV2 models are utilized. To determine the learning rate, the models are trained and evaluated at various Epochs. It's found that the accuracy of the max pooling and average pooling layers is 97.49% and 95.48%, respectively. And MobileNetV2 outperforms the other two models with the most remarkable accuracy of 99.67% with a data loss rate of 1.24.

show abstract

Section: Cnn Classifiermentioning

confidence: 99%

Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification

Tasnim¹,

Chakraborty²,

Shamrat³

et al. 2021

IJACSA

View full text Add to dashboard Cite

show abstract

“…The fully connected layer is usually used as a classifier of CNN, but too many parameters of the fully connected layer will increase the calculation amount of the network and thus slow down the training speed and also easily appear the overfitting problem [38]. Global average pooling (GAP) is a global average of all pixels in the feature map of each channel and obtains the output of each feature map [39][40][41]. GAP directly removes the features of black box in the fully connected layer and gives each channel practical significance; then, the vectors composed of these output features will be sent to the classifier for classification directly [42].…”

Section: Global Averagementioning

confidence: 99%

A Novel Deep Convolutional Neural Network Based on ResNet‐18 and Transfer Learning for Detection of Wood Knot Defects

et al. 2021

View full text Add to dashboard Cite

Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves wood utilization. Traditional neural network techniques have not yet been employed for wood defect detection due to long training time, low recognition accuracy, and nonautomatical extraction of defect image features. In this work, a model (so-called ReSENet-18) for wood knot defect detection that combined deep learning and transfer learning is proposed. The “squeeze-and-excitation” (SE) module is firstly embedded into the “residual basic block” structure for a “SE-Basic-Block” module construction. This model has the advantages of the features that are extracted in the channel dimension, and it is fused in multiscale with original features. Instantaneously, the fully connected layer is replaced with a global average pooling; consequently, the model parameters could be reduced effectively. The experimental results show that the accuracy has reached 99.02%, meanwhile the training time is also reduced. It shows that the proposed deep convolutional neural network based on ReSENet-18 combined with transfer learning can improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.

show abstract

“…The medical vision community is currently actively conducting diagnosis using computer-aided diagnosis [16]. To improve the performance of computer-aided diagnosis, several deep learning algorithms have been developed and applied [17][18][19][20]. Various challenges for deep learning with open data sets have been identified [21,22].…”

Section: Comparison With Prior Workmentioning

confidence: 99%

Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment

Lee¹,

Chai²,

Joo³

et al. 2021

JMIR Med Inform

Self Cite

View full text Add to dashboard Cite

Background Federated learning is a decentralized approach to machine learning; it is a training strategy that overcomes medical data privacy regulations and generalizes deep learning algorithms. Federated learning mitigates many systemic privacy risks by sharing only the model and parameters for training, without the need to export existing medical data sets. In this study, we performed ultrasound image analysis using federated learning to predict whether thyroid nodules were benign or malignant. Objective The goal of this study was to evaluate whether the performance of federated learning was comparable with that of conventional deep learning. Methods A total of 8457 (5375 malignant, 3082 benign) ultrasound images were collected from 6 institutions and used for federated learning and conventional deep learning. Five deep learning networks (VGG19, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) were used. Using stratified random sampling, we selected 20% (1075 malignant, 616 benign) of the total images for internal validation. For external validation, we used 100 ultrasound images (50 malignant, 50 benign) from another institution. Results For internal validation, the area under the receiver operating characteristic (AUROC) curve for federated learning was between 78.88% and 87.56%, and the AUROC for conventional deep learning was between 82.61% and 91.57%. For external validation, the AUROC for federated learning was between 75.20% and 86.72%, and the AUROC curve for conventional deep learning was between 73.04% and 91.04%. Conclusions We demonstrated that the performance of federated learning using decentralized data was comparable to that of conventional deep learning using pooled data. Federated learning might be potentially useful for analyzing medical images while protecting patients’ personal information.

show abstract

Channel Attention Module with Multi-scale Grid Average Pooling for Breast Cancer Segmentation in an Ultrasound Image

Cited by 86 publications

References 32 publications

Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification

Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification

A Novel Deep Convolutional Neural Network Based on ResNet‐18 and Transfer Learning for Detection of Wood Knot Defects

Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment

Contact Info

Product

Resources

About