皮膚癌的AI診斷課件

1、PPT制作、主講 朱朱明哲明哲文獻(xiàn)查找、翻譯、Word文檔制作 李卓李卓生物醫(yī)學(xué)傳感領(lǐng)域的新突破生物醫(yī)學(xué)傳感領(lǐng)域的新突破百度搜索引擎都膜拜的第一百度搜索引擎都膜拜的第一作者作者榮獲斯坦福大學(xué)榮獲斯坦福大學(xué)AI領(lǐng)域的領(lǐng)域的理學(xué)博士理學(xué)博士并修德克薩斯大學(xué)奧斯汀并修德克薩斯大學(xué)奧斯汀分校電子計(jì)算機(jī)工程及純分校電子計(jì)算機(jī)工程及純數(shù)學(xué)雙專業(yè)數(shù)學(xué)雙專業(yè)Dermatologist-level classification of skin cancerwith deep neural networks利用深度神經(jīng)網(wǎng)絡(luò)，進(jìn)行皮膚科專家級(jí)別的皮膚癌診斷這項(xiàng)研究成果被x-mol官網(wǎng)評(píng)為2017年年2月全球科學(xué)技術(shù)

2、十大突破之五月全球科學(xué)技術(shù)十大突破之五這種所謂的深度神經(jīng)網(wǎng)絡(luò)這種所謂的深度神經(jīng)網(wǎng)絡(luò)就是近年來經(jīng)常提及就是近年來經(jīng)常提及 但但又充滿神秘的又充滿神秘的從數(shù)學(xué)上看從數(shù)學(xué)上看深度神經(jīng)網(wǎng)絡(luò)深度神經(jīng)網(wǎng)絡(luò)(Deep Neural Networks)卷積神經(jīng)網(wǎng)絡(luò)卷積神經(jīng)網(wǎng)絡(luò)(Convolutional Neural Networks)淺析淺析CNNsLogistic回歸模型淺析淺析CNNs卷積的處理看起來使得問題復(fù)雜了，這是否會(huì)增大計(jì)算難度，降低效率？課堂討論環(huán)節(jié)顯然讓課堂效率提高了而它正是卷積過程的體現(xiàn)Layer L1在座近100位同學(xué)都提出了自己的觀點(diǎn)Layer L2通過小組討論，初步得出問題的解答La

3、yer L3對(duì)每個(gè)小組的討論結(jié)果進(jìn)行評(píng)估，考慮其對(duì)問題的偏離程度（權(quán)重），得出最終結(jié)果CNNs的工作就是將全局映射轉(zhuǎn)化為區(qū)域映射的過程。另一個(gè)現(xiàn)實(shí)中的例子是圖像處理Classical diagnosing methodInitial clinical screening Dermoscopic analysisBiopsyHistopathological examinationObserving undermicroscopeDiagnosticreportFormation of AI dermatologist A process of machine learningFigure 1

4、| Deep CNN layout. Formed by Google Inception v3 CNN architectureStep1 They demonstrated classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs.Figure 2 | A schematic illustration of the taxonomy and example test set

5、 imagesa, A subset of the top of the tree-structured taxonomy of skin disease.b, Malignant and benign example images from two disease classes.Step 2 They trained a CNN using a dataset of 129,450 clinical images, consisting of 2,032 different diseases.Step 3 Grasp the characteristic message of pictur

6、es by analyzing pixels.Figure 3 | Skin cancer classification performance of dermatologists (a) and CNN (b).Practical diagnose Analyzing up-loaded picture from mobile devicesFigure 4 | t-SNE visualization of the last hidden layer representations in the CNN for four disease classesReal case analysis a

7、s support information Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 and can therefore potentially provide low-cost universal access to

8、 vital diagnostic care.Ericsson Mobility Report, 2016 Deep learning algorithms, powered by advances in computation and very large datasets, have recently been shown to exceed human performance in visual tasks such as playing Atari games, strategic board games like Go and object recognition.AI應(yīng)用于生活的幾個(gè)例子2017年5月23日5月27日 “第二次圍棋人機(jī)大戰(zhàn)” 論決策和價(jià)值評(píng)估，人類論決策和價(jià)值評(píng)估，人類真的輸了！真的輸了！Demis Hassabis，創(chuàng)業(yè)公司