Reversed Active Learning based Atrous DenseNet for Pathological Image Classification

6 Jul 2018 Yuexiang Li Xinpeng Xie Linlin Shen Shaoxiong Liu

Witnessed the development of deep learning in recent years, increasing number of researches try to adopt deep learning model for medical image analysis. However, the usage of deep learning networks for the pathological image analysis encounters several challenges, e.g. high resolution (gigapixel) of pathological images and lack of annotations of cancer areas... (read more)

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Methods used in the Paper


METHOD TYPE
Convolution
Convolutions
Average Pooling
Pooling Operations
Concatenated Skip Connection
Skip Connections
Global Average Pooling
Pooling Operations
Kaiming Initialization
Initialization
1x1 Convolution
Convolutions
Batch Normalization
Normalization
ReLU
Activation Functions
Dropout
Regularization
Dense Connections
Feedforward Networks
Max Pooling
Pooling Operations
Softmax
Output Functions
DenseNet
Convolutional Neural Networks
Dense Block
Image Model Blocks