深度學(xué)習(xí)與新冠肺炎-v2

講師：王司圖深度學(xué)習(xí)與新冠肺炎人工智能在醫(yī)療領(lǐng)域應(yīng)用CRT（陰極射線管）顯示器CathodeRay（陰極射線）倫琴射線（X光）X光用于醫(yī)療tomos=slice,sectionCT(ComputedTomography)試劑盒有限準(zhǔn)確率較低（30-50%）需多次檢測(cè)檢測(cè)時(shí)間長(zhǎng)（1天以上）核酸檢測(cè)新冠肺炎CT與新冠肺炎COVID-19肺炎特征磨玻璃影胸膜凹陷征新型冠狀病毒感染的肺炎診療方案(試行第五版)CT與新冠肺炎深度學(xué)習(xí)與新冠肺炎網(wǎng)絡(luò)架構(gòu)卷積(convolution)0001111100010101011111010卷積(convolution)0101110100001111100010101011111010卷積(convolution)01011101040001111100010101011111010卷積(convolution)010111010420001111100010101011111010卷積(convolution)0101110104230001111100010101011111010卷積(convolution)01011101042320001111100010101011111010卷積(convolution)010111010423240001111100010101011111010卷積(convolution)0101110104232420001111100010101011111010卷積(convolution)01011101042324240001111100010101011111010卷積(convolution)010111010423242420001111100010101011111010卷積(convolution)010111010423242425卷積(convolution)-1-1-1-18-1-1-1-1卷積(convolution)-1-1-1-18-1-1-1-1卷積(convolution)121242121

卷積(convolution)121242121

0000000000011001110000010100010111001101000000000Convpadded01011101014232424250000000000011001110000010100010111001101000000000Convpadded01011101011423242425池化(pooling)MaxpoolingAvgpooling4323214534455667432321453445566745672.53.54.55.50001111100010101011111010Conv(strides=2)01011101040001111100010101011111010Conv(strides=2)010111010430001111100010101011111010Conv(strides=2)0101110104340001111100010101011111010Conv(strides=2)0101110104345InceptionInceptionmodule(netinnet)3x3conv5x5conv3x3pool上層合并1x1conv減少神經(jīng)網(wǎng)絡(luò)參數(shù)整合不同通道信息稀疏矩陣->密集矩陣，加快收斂速度InceptionmoduleInceptionmodulewithKerasInceptionmodulewithKerasInceptionmodulewithKerasInceptionnetworkwithKeras數(shù)據(jù)樣本44

新冠肺炎患者55典型肺炎患者數(shù)據(jù)樣本195正樣本258負(fù)樣本數(shù)據(jù)樣本237

訓(xùn)練樣本216

驗(yàn)證樣本結(jié)果評(píng)估Validation驗(yàn)證Internal(%)External(%)Accuracy準(zhǔn)確率82.973.1Specificity特異度80.567Sensitivity靈敏度8474準(zhǔn)確率：被識(shí)別出的樣本數(shù)/總樣本數(shù)特異度：被識(shí)別出的負(fù)樣本數(shù)/總負(fù)樣本數(shù)靈敏度：被識(shí)別出的正樣本數(shù)/總正樣本數(shù)結(jié)果評(píng)估Validation驗(yàn)證Internal(%)External(%)Accuracy準(zhǔn)確率82.973.1Specificity特異度80.567Sensitivity靈敏度8474模型局限訓(xùn)練數(shù)據(jù)集規(guī)模小人工標(biāo)注資源稀缺Adeeplearningalgorithmusing