Info

此问题已关闭。 请重新打开它进行编辑或回答。

How to generate ROC curve for multiclass semantic segmentation of biological tissue

1 次查看（过去 30 天）
Zandile Sityata
Zandile Sityata 2022-5-9
关闭： Stephan 2022-5-9
How would I obtain ROC curve based on https://www.mathworks.com/help/vision/ref/semanticseg.html?s_tid=d ?
My code looks something like this:
inputSize = [24 32 3];
imgLayer = imageInputLayer(inputSize)
filterSize = 3;
numFilters = 32;
conv = convolution2dLayer(filterSize,numFilters,'Padding',1);
relu = reluLayer();
poolSize = 2;
maxPoolDownsample2x = maxPooling2dLayer(poolSize,'Stride',2);
downsamplingLayers = [
conv
relu
maxPoolDownsample2x
conv
relu
maxPoolDownsample2x
]
filterSize = 4;
transposedConvUpsample2x = transposedConv2dLayer(4,numFilters,'Stride',2,'Cropping',1);
upsamplingLayers = [
transposedConvUpsample2x
relu
transposedConvUpsample2x
relu
]
numClasses = 5;
conv1x1 = convolution2dLayer(1,numClasses);
finalLayers = [
conv1x1
softmaxLayer()
pixelClassificationLayer()
]
net = [
imgLayer
downsamplingLayers
upsamplingLayers
finalLayers
]
imgLayer =
ImageInputLayer with properties:
Name: ''
InputSize: [24 32 3]
Hyperparameters
DataAugmentation: 'none'
Normalization: 'zerocenter'
NormalizationDimension: 'auto'
Mean: []
downsamplingLayers =
6×1 Layer array with layers:
1 '' Convolution 32 3×3 convolutions with stride [1 1] and padding [1 1 1 1]
2 '' ReLU ReLU
3 '' Max Pooling 2×2 max pooling with stride [2 2] and padding [0 0 0 0]
4 '' Convolution 32 3×3 convolutions with stride [1 1] and padding [1 1 1 1]
5 '' ReLU ReLU
6 '' Max Pooling 2×2 max pooling with stride [2 2] and padding [0 0 0 0]
upsamplingLayers =
4×1 Layer array with layers:
1 '' Transposed Convolution 32 4×4 transposed convolutions with stride [2 2] and cropping [1 1 1 1]
2 '' ReLU ReLU
3 '' Transposed Convolution 32 4×4 transposed convolutions with stride [2 2] and cropping [1 1 1 1]
4 '' ReLU ReLU
finalLayers =
3×1 Layer array with layers:
1 '' Convolution 5 1×1 convolutions with stride [1 1] and padding [0 0 0 0]
2 '' Softmax softmax
3 '' Pixel Classification Layer Cross-entropy loss
net =
14×1 Layer array with layers:
1 '' Image Input 24×32×3 images with 'zerocenter' normalization
2 '' Convolution 32 3×3 convolutions with stride [1 1] and padding [1 1 1 1]
3 '' ReLU ReLU
4 '' Max Pooling 2×2 max pooling with stride [2 2] and padding [0 0 0 0]
5 '' Convolution 32 3×3 convolutions with stride [1 1] and padding [1 1 1 1]
6 '' ReLU ReLU
7 '' Max Pooling 2×2 max pooling with stride [2 2] and padding [0 0 0 0]
8 '' Transposed Convolution 32 4×4 transposed convolutions with stride [2 2] and cropping [1 1 1 1]
9 '' ReLU ReLU
10 '' Transposed Convolution 32 4×4 transposed convolutions with stride [2 2] and cropping [1 1 1 1]
11 '' ReLU ReLU
12 '' Convolution 5 1×1 convolutions with stride [1 1] and padding [0 0 0 0]
13 '' Softmax softmax
14 '' Pixel Classification Layer Cross-entropy loss
>> dataSetDir = fullfile('C:\Users\a0050627\Pictures\3D reconstruction stacks\Final dissertation images\final dissertation images\ground truth 13 32h24w\trainingImages');
imageDir = fullfile('C:\Users\a0050627\Pictures\3D reconstruction stacks\Final dissertation images\final dissertation images\ground truth 13 32h24w\trainingImages');
labelDir = fullfile('C:\Users\a0050627\Pictures\3D reconstruction stacks\Final dissertation images\final dissertation images\ground truth 13 32h24w\trainingLabels');
>> imds = imageDatastore(imageDir);
classNames = ["Fibrous_tissue" "Cartilage" "Blood_cells" "Smooth_muscle" "Background"];
labelIDs = [1 2 3 4 5];
pxds = pixelLabelDatastore(labelDir,classNames,labelIDs);
I = read(imds);
C = read(pxds);
>> I = imresize(I,5);
L = imresize(uint8(C{1}),5);
imshowpair(I,L,'montage')
numFilters = 56;
filterSize = 3;
numClasses = 5;
layers = [
imageInputLayer([24 32 3])
convolution2dLayer(filterSize,numFilters,'Padding',1)
reluLayer()
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(filterSize,numFilters,'Padding',1)
reluLayer()
transposedConv2dLayer(4,numFilters,'Stride',2,'Cropping',1);
convolution2dLayer(1,numClasses);
softmaxLayer()
pixelClassificationLayer()
];
opts = trainingOptions('sgdm', ...
'InitialLearnRate',1e-3, ...
'MaxEpochs',100, ...
'MiniBatchSize',56);
trainingData = combine(imds,pxds);
net = trainNetwork(trainingData,layers,opts);
testImage = imread('trainingImages0000.tif');
imshow(testImage)
C = semanticseg(testImage,net);
B = labeloverlay(testImage,C);
imshow(B)
Training on single CPU.
Initializing input data normalization.
|========================================================================================|
| Epoch | Iteration | Time Elapsed | Mini-batch | Mini-batch | Base Learning |
| | | (hh:mm:ss) | Accuracy | Loss | Rate |
|========================================================================================|
| 1 | 1 | 00:00:00 | 16.08% | 2.4888 | 0.0010 |
| 50 | 50 | 00:00:03 | 69.22% | 0.8458 | 0.0010 |
| 100 | 100 | 00:00:05 | 73.94% | 0.6949 | 0.0010 |
|========================================================================================|
Error using imread>get_full_filename (line 570)
File "trainingImages0000.tif" does not exist.
Error in imread (line 377)
fullname = get_full_filename(filename);
>> I = imresize(I,5);
L = imresize(uint8(C{1}),5);
imshowpair(I,L,'montage')
numFilters = 56;
filterSize = 3;
numClasses = 5;
layers = [
imageInputLayer([24 32 3])
convolution2dLayer(filterSize,numFilters,'Padding',1)
reluLayer()
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(filterSize,numFilters,'Padding',1)
reluLayer()
transposedConv2dLayer(4,numFilters,'Stride',2,'Cropping',1);
convolution2dLayer(1,numClasses);
softmaxLayer()
pixelClassificationLayer()
];
opts = trainingOptions('sgdm', ...
'InitialLearnRate',1e-3, ...
'MaxEpochs',100, ...
'MiniBatchSize',56);
trainingData = combine(imds,pxds);
net = trainNetwork(trainingData,layers,opts);
testImage = imread('trainingImages0000.tif');
imshow(testImage)
C = semanticseg(testImage,net);
B = labeloverlay(testImage,C);
imshow(B)
Training on single CPU.
Initializing input data normalization.
|========================================================================================|
| Epoch | Iteration | Time Elapsed | Mini-batch | Mini-batch | Base Learning |
| | | (hh:mm:ss) | Accuracy | Loss | Rate |
|========================================================================================|
| 1 | 1 | 00:00:00 | 25.46% | 2.5070 | 0.0010 |
| 50 | 50 | 00:00:03 | 71.97% | 0.8210 | 0.0010 |
| 100 | 100 | 00:00:05 | 75.99% | 0.6633 | 0.0010 |
|========================================================================================|
>> dataSetDir = fullfile('C:\Users\a0050627\Pictures\3D reconstruction stacks\Final dissertation images\final dissertation images\ground truth 13 32h24w\dataSetDir');
testImagesDir = fullfile('C:\Users\a0050627\Pictures\3D reconstruction stacks\Final dissertation images\final dissertation images\ground truth 13 32h24w\trainingImages');
imds = imageDatastore(testImagesDir);
testLabelsDir = fullfile('C:\Users\a0050627\Pictures\3D reconstruction stacks\Final dissertation images\final dissertation images\ground truth 13 32h24w\trainingLabels');
classNames = ["Fibrous_tissue" "Cartilage" "Blood_cells" "Smooth_muscle" "Background"];
labelIDs = [1 2 3 4 5];
pxdsTruth = pixelLabelDatastore(testLabelsDir,classNames,labelIDs);
pxdsResults = semanticseg(imds,net,"WriteLocation",tempdir);
metrics = evaluateSemanticSegmentation(pxdsResults,pxdsTruth);
metrics.ClassMetrics
metrics.ConfusionMatrix
normConfMatData = metrics.NormalizedConfusionMatrix.Variables;
figure
h = heatmap(classNames,classNames,100*normConfMatData);
h.XLabel = 'Predicted Class';
h.YLabel = 'True Class';
h.Title = 'Normalized Confusion Matrix (%)';
imageIoU = metrics.ImageMetrics.MeanIoU;
figure
histogram(imageIoU)
title('Image Mean IoU')
[minIoU, worstImageIndex] = min(imageIoU);
minIoU = minIoU(1);
worstImageIndex = worstImageIndex(1);
worstTestImage = readimage(imds,worstImageIndex);
worstTrueLabels = readimage(pxdsTruth,worstImageIndex);
worstPredictedLabels = readimage(pxdsResults,worstImageIndex);
worstTrueLabelImage = im2uint8(worstTrueLabels == classNames(1));
worstPredictedLabelImage = im2uint8(worstPredictedLabels == classNames(1));
worstMontage = cat(4,worstTestImage,worstTrueLabelImage,worstPredictedLabelImage);
worstMontage = imresize(worstMontage,4,"nearest");
figure
montage(worstMontage,'Size',[1 3])
title(['Test Image vs. Truth vs. Prediction. IoU = ' num2str(minIoU)])
[maxIoU, bestImageIndex] = max(imageIoU);
maxIoU = maxIoU(1);
bestImageIndex = bestImageIndex(1);
bestTestImage = readimage(imds,bestImageIndex);
bestTrueLabels = readimage(pxdsTruth,bestImageIndex);
bestPredictedLabels = readimage(pxdsResults,bestImageIndex);
bestTrueLabelImage = im2uint8(bestTrueLabels == classNames(1));
bestPredictedLabelImage = im2uint8(bestPredictedLabels == classNames(1));
bestMontage = cat(4,bestTestImage,bestTrueLabelImage,bestPredictedLabelImage);
bestMontage = imresize(bestMontage,4,"nearest");
figure
montage(bestMontage,'Size',[1 3])
title(['Test Image vs. Truth vs. Prediction. IoU = ' num2str(maxIoU)])
evaluationMetrics = ["accuracy" "iou"];
metrics = evaluateSemanticSegmentation(pxdsResults,pxdsTruth,"Metrics",evaluationMetrics);
metrics.ClassMetrics
Running semantic segmentation network
-------------------------------------
* Processed 8 images.
Evaluating semantic segmentation results
----------------------------------------
* Selected metrics: global accuracy, class accuracy, IoU, weighted IoU, BF score.
* Processed 8 images.
* Finalizing... Done.
* Data set metrics:
GlobalAccuracy MeanAccuracy MeanIoU WeightedIoU MeanBFScore
______________ ____________ _______ ___________ ___________
0.7609 0.42784 0.34059 0.6219 0.49539
ans =
5×3 table
Accuracy IoU MeanBFScore
________ _______ ___________
Fibrous_tissue 0 0 NaN
Cartilage 0.41071 0.31293 0.44522
Blood_cells 0.20241 0.16634 0.27843
Smooth_muscle 0.61982 0.45857 0.57353
Background 0.90628 0.7651 0.68441
ans =
5×5 table
Fibrous_tissue Cartilage Blood_cells Smooth_muscle Background
______________ _________ ___________ _____________ __________
Fibrous_tissue 0 3 6 6 0
Cartilage 0 46 37 28 1
Blood_cells 0 20 84 273 38
Smooth_muscle 0 12 42 1151 652
Background 0 0 5 346 3394
Looking forward to a response,
Zandile

此问题已关闭。

回答（0 个）

此问题已关闭。

标签

产品


版本

R2020b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by