A Dataset of Laryngeal Endoscopic Images with Comparative Study on Convolution Neural Network Based Semantic Segmentation

Purpose Automated segmentation of anatomical structures in medical image analysis is a prerequisite for autonomous diagnosis as well as various computer and robot aided interventions. Recent methods based on deep convolutional neural networks (CNN) have outperformed former heuristic methods... (read more)

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Datasets


Introduced in the Paper:

VocalFolds

Mentioned in the Paper:

Cityscapes

Results from the Paper


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Methods used in the Paper


METHOD TYPE
Dilated Convolution
Convolutions
1x1 Convolution
Convolutions
Convolution
Convolutions
ENet Dilated Bottleneck
Image Model Blocks
ENet Bottleneck
Image Model Blocks
ENet Initial Block
Image Model Blocks
Kaiming Initialization
Initialization
Batch Normalization
Normalization
ReLU
Activation Functions
SpatialDropout
Regularization
PReLU
Activation Functions
Max Pooling
Pooling Operations
Softmax
Output Functions
ENet
Semantic Segmentation Models
SegNet
Semantic Segmentation Models