18 Questions
How many convolution layers are there in total in the VGG16 network model?
13
What is the key feature of the Squeeze-and-Excitation (SE) module?
Adopts common pooling and fully connected layers internally
What is the purpose of stacking multiple identical convolutions in the convolutional blocks?
To extract more complex features
How many convolution layers make up the first two convolution blocks in the VGG16 model?
Two
What operation is part of the Squeeze-and-Excitation (SE) module to emphasize important features?
Squeeze operation
How does the structure of VGG16 deepen the network's depth?
Through the repeated stacking of convolutional blocks
What is the main focus of the research mentioned in the text?
Improving breast cancer histopathology image classification
How does the method in the research save time during classification?
By organizing instance images into packages with labels
What is the classification accuracy achieved by the method described in the text?
Between 83% and 87%
What is the primary purpose of using VGG16 in the research?
To classify benign and malignant breast cancer pathology images
Which section of the article discusses the experiments and their results?
Section III
What is the structure of the VGG16 network mentioned in the text?
Five convolution blocks
Which model proposed in the paper showed better performance compared to ResNet, SE-ResNet, DenseNet, and the original VGG16 model?
SE-VGG16
What was the recognition rate achieved by the SE-VGG16 model on the breast cancer histopathology image dataset BreakHis for the benign-malignant dichotomous classification task of breast tumors?
98.41%
How much percentage point improvement did the SE-VGG16 model achieve over the single VGG16 model in recognizing breast cancer histopathology images as benign-malignant?
5.68
What learning rate was used to train ResNet, SE-ResNet, DenseNet, VGG16, and the improved VGG16 proposed in this paper?
0.01
Which model had lower recognition accuracy compared to the algorithms discussed in this paper?
VGG16
How many epochs were used to train ResNet, SE-ResNet, DenseNet, VGG16, and the improved VGG16 proposed in this paper?
500
Study Notes
Comparison Results
- ResNet, SE-ResNet, DenseNet, and VGG16 have lower recognition accuracy than the algorithms in this paper
- Comparison of recognition rates:
- ResNet: 81.6%
- SE-ResNet: 89.26%
- DenseNet: 91.67%
- VGG16: 92.73%
Training and Models
- Training parameters:
- Learning rate: 0.01
- Number: 2
- Epochs: 500
- Batch size: 64
- The improved VGG16 model proposed in this paper outperforms ResNet, SE-ResNet, DenseNet, and the original VGG16 model
SE-VGG16 Model
- The SE-VGG16 model has a higher recognition rate of 98.41% on the breast cancer histopathology image dataset BreakHis
- Improvement of 16.8, 9.15, and 5.68 percentage points over the single ResNet, DenseNet, and VGG16 models, respectively
Multi-Instance Learning
- Multi-instance learning was introduced in breast cancer histopathology image classification
- Organizing instance images into packages and combining CNNs with specific loss functions
- Saving time required for instance labeling and achieving classification accuracies between 83% and 87%
VGG Network
- VGG16 network structure diagram shown in Fig. 1
- The network can be divided into five convolution blocks
- Each convolution block uses a convolution core of size 3
- The stacking of multiple identical convolutions in the convolutional blocks can extract more complex features
Improved VGG16 Model
- The SE module (Squeeze-and-Excitation block) is added after the convolutional layer of the VGG network model
- The SE module has strong generality and can be easily embedded into other common network models
Test your knowledge on the architecture of Convolutional Neural Networks. Understand the composition of convolution blocks and the number of convolution layers used in each block to extract features.
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