A 2017 Guide to Semantic Segmentation with Deep Learning 笔记
2018-03-23 17:26
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原文A 2017 Guide to Semantic Segmentation with Deep Learning
0. Intro
1. Problem
1.1 before deep
1.2 current
1.3 postprocessing
2. Models
why medical images are different from natural images
dataset: VOC2012, MSCOCO
metric: mean IOU
before deep
textonforest
random forest based classifier
prob
classifaction fixed input size
pooling layer: discard ‘where’ infomation
patch classication
classification networks usually have full connected layers and therefore required fixed size images.
1.2 current
FCN(prob1)
allow segmentation on any size image
Pooling(prob2)
encoder-decoder arch
dilated conv
encoder-decoder
encoder: reduces the spatial dimension with pooling layer
decoder: recover object details and spatial dimension
shortcut connections: help decoder recover the object details better
dilated conv
away with pooling layers
1.3 postprocessing
CRF postprocessing
similar intensity pixels tend to be labeled as same class
boost scores by 1-2%
0. Intro
1. Problem
1.1 before deep
1.2 current
1.3 postprocessing
2. Models
0. Intro
mainly use natural/real world image datasetswhy medical images are different from natural images
dataset: VOC2012, MSCOCO
metric: mean IOU
1. Problem
1.1 before deepbefore deep
textonforest
random forest based classifier
prob
classifaction fixed input size
pooling layer: discard ‘where’ infomation
patch classication
classification networks usually have full connected layers and therefore required fixed size images.
1.2 current
FCN(prob1)
allow segmentation on any size image
Pooling(prob2)
encoder-decoder arch
dilated conv
encoder-decoder
encoder: reduces the spatial dimension with pooling layer
decoder: recover object details and spatial dimension
shortcut connections: help decoder recover the object details better
dilated conv
away with pooling layers
1.3 postprocessing
CRF postprocessing
similar intensity pixels tend to be labeled as same class
boost scores by 1-2%
2. Models
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