Multiple Input Single Output Segmentation using Deep Learning

Question

Koshy 2019-4-27

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/458900-multiple-input-single-output-segmentation-using-deep-learning

评论：马瑞李 2021-1-21

I have 4 modal volumetric image data and output segemented data. I have to create a multi input DAG network, and I have succesfully created it using lgraph..

But I cannot able to train the network using trainNetwork. It shows error that only one input can be feed to trainNetwork..

My code is below, store1, store2, store3, store4 are four input 3d datastore and pxd is the output datastore

inputSize = [64 64 64];

layers1 = [

image3dInputLayer(inputSize,'Normalization','none','Name','input1')

convolution3dLayer(3,155,'Padding','same','Name','conv_11')

maxPooling3dLayer(4,'Name','maxpool1')];

layers2=[

image3dInputLayer(inputSize,'Normalization','none','Name','input2')

convolution3dLayer(3,155,'Padding','same','Name','conv_21')

maxPooling3dLayer(4,'Name','maxpool2')];

layers3=[

image3dInputLayer(inputSize,'Normalization','none','Name','input3')

convolution3dLayer(3,155,'Padding','same','Name','conv_31')

maxPooling3dLayer(4,'Name','maxpool3')];

layers4=[

image3dInputLayer(inputSize,'Normalization','none','Name','input4')

convolution3dLayer(3,155,'Padding','same','Name','conv_41')

maxPooling3dLayer(4,'Name','maxpool4')];

concat1=concatenationLayer(3,4,'Name','depth_1');

outlayer=[

transposedConv3dLayer(3,620,'stride',2,'cropping','same','Name','tconv_o1')

convolution3dLayer(1,numLabels,'Name','convLast');

softmaxLayer('Name','softmax');

dicePixelClassification3dLayer('output')];

lgraph = layerGraph;

lgraph = addLayers(lgraph,layers1);

lgraph = addLayers(lgraph,layers2);

lgraph = addLayers(lgraph,layers3);

lgraph = addLayers(lgraph,layers4);

lgraph = addLayers(lgraph,concat1);

lgraph = addLayers(lgraph,outlayer);

lgraph = connectLayers(lgraph,'maxpool1','depth_1/in1');

lgraph = connectLayers(lgraph,'maxpool2','depth_1/in2');

lgraph = connectLayers(lgraph,'maxpool3','depth_1/in3');

lgraph = connectLayers(lgraph,'maxpool4','depth_1/in4');

lgraph = connectLayers(lgraph,'depth_1','tconv_o1');

plot(lgraph)

miniBatchSize = 1;

options = trainingOptions('rmsprop', ...

'MaxEpochs',1, ...

'InitialLearnRate',0.01, ...

'LearnRateSchedule','piecewise', ...

'LearnRateDropPeriod',5, ...

'LearnRateDropFactor',0.95, ...

'Plots','training-progress', ...

'Verbose',false, ...

'MiniBatchSize',miniBatchSize);

[net,info] = trainNetwork({store1,store2,store3,store4},pxds,lgraph,options);

Error shown is

Error in line:

[net,info] = trainNetwork({store1,store2,store3,store4},pxds,lgraph,options);

Caused by:

Network: Too many input layers. The network must have one input layer.

Detected input layers:

layer 'input1'

layer 'input2'

layer 'input3'

layer 'input4'

Please help me to solve this problem or suggest another way to train multi input image data

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Answer 1

gonzalo Mier 2019-4-28

1
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/458900-multiple-input-single-output-segmentation-using-deep-learning#answer_372608

This question was asked here: https://www.mathworks.com/matlabcentral/answers/369328-how-to-use-multiple-input-layers-in-dag-net-as-shown-in-the-figure

I will copy and paste the answer of Mahmoud Afifi:

"One idea is to feed the network with concatenated inputs (e.g., image1;image2) then create splitter layers that split each input. The problem here is that you have to feed the network with .mat files, not image paths. Another idea is to store your images as tiff files which can hold 4 channels. In this case, you can store a colored image (3 channel) and a grayscale one. Have a look at this example https://www.mathworks.com/matlabcentral/fileexchange/65065-two-stream-cnn-for-gender-recognition-using-hand-images?s_tid=FX_rc1_behav .. see twoStream.m file. "