分类肿瘤图像多分辨率阻塞

dataDir = fullfile (tempdir,“Camelyon16”);负载(fullfile (dataDir“trainingAndValidationDatastores.mat”))

网= inceptionv3;lgraph = layerGraph(净);

[learnableLayer, classLayer] = findLayersToReplace (lgraph);

numClasses = 2;newLearnableLayer = fullyConnectedLayer (numClasses Name =“预测”);lgraph = replaceLayer (lgraph learnableLayer.Name newLearnableLayer);

newClassLayer = classificationLayer (Name =“ClassificationLayer_predictions”);lgraph = replaceLayer (lgraph classLayer.Name newClassLayer);

选择= trainingOptions (“rmsprop”,…MaxEpochs = 1,…MiniBatchSize = 256,…洗牌=“every-epoch”,…ValidationFrequency = 250,…InitialLearnRate = 1的军医,…SquaredGradientDecayFactor = 0.99,…ResetInputNormalization = false,…情节=“训练进步”);

doTraining =假;如果doTraining checkpointsDir = fullfile (dataDir,“检查点”);如果~存在(checkpointsDir“dir”mkdir (checkpointsDir);结束options.CheckpointPath = checkpointsDir;options.ValidationData = dsValLabeled;trainedNet = trainNetwork (dsTrainLabeled、lgraph选项);modelDateTime =字符串(datetime (“现在”格式=“yyyy-MM-dd-HH-mm-ss”));保存(dataDir +“trainedCamelyonNet——”+ modelDateTime +“.mat”,“trainedNet”);其他的trainedCamelyonNet_url =“//www.tianjin-qmedu.com/万博1manbetxsupportfiles/vision/data/trainedCamelyonNet.mat”;dataDir = fullfile (tempdir,“Camelyon16”);downloadTrainedNetwork (trainedCamelyonNet_url dataDir);负载(fullfile (dataDir“trainedCamelyonNet.mat”));结束

testDir = fullfile (dataDir,“测试”);testImageDir = fullfile (testDir,“图片”);testAnnotationDir = fullfile (testDir,“lesion_annotations”);如果~存在(testDir“dir”mkdir (testDir);mkdir (fullfile (testDir,“图片”));mkdir (fullfile (testDir,“lesion_annotations”));结束

testFileSet = matlab.io.datastore.FileSet (testImageDir + filesep +“测试*”);testImages = blockedImage (testFileSet);

testImages = setSpatialReferencingForCamelyon16 (testImages);

normalMaskLevel = 8;testDir = fullfile (dataDir,“测试”);testTissueMaskDir = fullfile (testDir,“test_tissue_mask_level”+ num2str (normalMaskLevel));如果~ isfolder (testTissueMaskDir) testTissueMasks =应用(testImages, @ (bs) im2gray (bs.Data) < 150…BlockSize = (512 512),…= normalMaskLevel水平,…UseParallel = canUseGPU,…DisplayWaitbar = false,…OutputLocation = testTissueMaskDir);保存(fullfile (testTissueMaskDir“testTissueMasks.mat”),“testTissueMasks”)其他的%加载以前保存的数据负载(fullfile (testTissueMaskDir“testTissueMasks.mat”),“testTissueMasks”);结束

browseBlockedImages (testTissueMasks, 1);

tumorMaskLevel = 8;

testTumorMaskDir = fullfile (testDir, (“test_tumor_mask_level”num2str (tumorMaskLevel)]);如果~ isfolder (testTumorMaskDir) testTumorMasks = createMaskForCamelyon16TumorTissue (testImages, testAnnotationDir, testTumorMaskDir tumorMaskLevel);保存(fullfile (testTumorMaskDir“testTumorMasks.mat”),“testTumorMasks”)其他的负载(fullfile (testTumorMaskDir“testTumorMasks.mat”),“testTumorMasks”);结束

numTest =元素个数(testImages);outputHeatmapsDir = fullfile (testDir,“热图”);networkBlockSize = (299299 3);抽搐为印第安纳州= 1:numTest气管无名动脉瘘管的%检查文件已经存在[~,id] = fileparts (testImages(印第安纳州).Source);输出文件= fullfile (outputHeatmapsDir id +“.tif”);如果~存在(输出文件,“文件”)劳工统计局= selectBlockLocations (testImages(印第安纳州),水平= 1,…BlockSize = networkBlockSize,…掩码= testTissueMasks(印第安纳州),InclusionThreshold = 0);%热图是内存blockedImage对象贝克海姆=应用(testImages(印第安纳州),@ (x) predictBlockForCamelyon16 (x, trainedNet),…水平= 1,BlockLocationSet = bls BatchSize = 128,…PadPartialBlocks = true, DisplayWaitBar = false);气管无名动脉瘘管的%结果写入一个文件写(贝克海姆,输出文件,BlockSize = [512 - 512]);结束结束toc

heatMapFileSet = matlab.io.datastore.FileSet (outputHeatmapsDir FileExtensions =“.tif”);bheatMapImages = blockedImage (heatMapFileSet);

idx = 27个;图tiledlayout (1、2) nexttile hBim1 = showCamelyon16TumorAnnotations (testImages (idx) testAnnotationDir);标题(“地面实况”)

nexttile hBim2 = bigimageshow (bheatMapImages (idx)插值=“最近的”);colormap(飞机)

linkaxes ([hBim1.Parent hBim2.Parent]) xlim ylim((53982、65269))((122475、133762))标题(“预言的热图”)

打= 0.8;

为印第安纳州= 1:numTest bcmatrix(印第安纳州),bcmatrixImage{印第安纳}]=应用(bheatMapImages(印第安纳州),…@ (bs tumorMask tissueMask) computeBlockConfusionMatrixForCamelyon16(废话,tumorMask, tissueMask打),…ExtraImages = [testTumorMasks(印第安纳州),testTissueMasks(印第安纳州)]);结束

cmArray = arrayfun (@ (c)收集(c), bcmatrix);厘米=[和([cmArray.tp])和([cmArray.fp]);和([cmArray.fn])和([cmArray.tn])];

图confusionchart(厘米,“肿瘤”,“正常”),归一化=“total-normalized”)

idx = 27个;图tiledlayout (1、2) nexttile hBim1 = showCamelyon16TumorAnnotations (testImages (idx) testAnnotationDir);标题(“地面实况”)nexttile hBim2 = bigimageshow (testImages (idx));cmColormap = [0 0 0;0 1 1;1 1 0;1 0 0];showlabels (hBim2 bcmatrixImage {idx},…Colormap = cmColormap AlphaData = bcmatrixImage {idx})标题(“分类模块”)linkaxes ([hBim1.Parent hBim2.Parent]) xlim (63000 [56000]) ylim (132600 [125000])

约打= [1 0.99 0.9::。1 0.05 0];(tpr,玻璃钢,ppv) = computeROCCurvesForCamelyon16 (bheatMapImages、testTumorMasks testTissueMasks,打);

图楼梯(玻璃钢、tpr、“-”);ROCAUC = trapz(玻璃钢,tpr);标题([曲线下的面积:“num2str (ROCAUC)]);包含(“假阳性”)ylabel (“真阳性率”)