深度學習:拆解健康密碼

(文章於2018年8月16日在香港經濟日報刊登)

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用內窺鏡和大腸鏡檢查人體內部,始自1880年代,病理學家須接受長期訓練,用顯微鏡和侵入性測試辦別腫瘤。隨城市人口與內科疾病增加,診斷專家日漸吃緊,肉眼有時難免誤診。能否將深度學習 (Deep Learning) 技術用於診斷影像?

深度學習模仿人類腦部神經元互相連結,迅速處理及傳遞訊息。人工神經網絡利用層次、連結和方向傳遞數據,由第一層出發,每層進行不同工序,直到最後一層,數據成為輸出的洞見。分析醫療影像約需100層人工神經網絡。

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Cracking the Internal Health Code with Deep Learning

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The endoscope and colonoscope were first developed in 1880s to look inside the body. Specialists use their expertise and experience to examine the medical images. But sometimes, human error and backend issues can result in misdiagnosis.

Population increase and more cases of internal diseases are overloading the medical industry in many major cities in the world. In turn, the demand of medical specialists continues to soar.

However, training more medical specialists is not enough. Pathologists require long-term training and painstaking work before visually detecting abnormalities in tumor tissues by looking through the microscope and conducting invasive tests. Deep learning offers a better way to develop a diagnosis scope for medical inspection.

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深度學習:拆解健康密碼

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早在 1880 年代,醫學界已使用內窺鏡和大腸鏡檢查人體內部情況,由專家分析醫療影像,但診斷有時會因人為錯誤和後台因素出錯。

隨全球城市人口增長,內科疾病個案數字急速上升,公眾對診斷專家的需求殷切,單在數量上培訓專家並不足夠。病理學家須長時間接受訓練、努力不懈,透過觀察顯微鏡和侵入性測試,方能辦別腫瘤組織。深度學習 (Deep Learning) 有助醫療觀測診斷的方法更上一層樓。

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