跟住研究者要個網絡做學習:個研究者走去搵柞數據返嚟-呢柞數據就係所謂嘅訓練集(training set);呢個訓練集會包括咗一連串嘅輸入個案,每個個案都係一幅影肺嘅 X 光片,研究者亦已經知道啲個案當中邊個有肺癌邊個冇;跟手個研究者就可以將個訓練集嘅數據入落去個神經網絡做輸入,等部電腦計出喺每一個個案入面,個神經網絡俾出嚟嘅輸出(即係佢對「呢幅 X 光片個人係咪有肺癌」嘅診斷)同正確嘅輸出值有幾大出入,跟手事先編寫好嘅學習法則就會按照呢啲落差值計吓啲權重要點變,先可以令到個神經網絡俾出更準確嘅答案。如是者,喺個神經網絡運算過大量嘅個案之後,佢嗰啲權重等等嘅參數就會有所改變,變到更加能夠計到正確嘅答案-個研究者就可以攞呢個神經網絡嚟去做將來嘅肺癌診斷。呢種用電腦視覺做醫療診斷嘅做法喺廿一世紀醫學上經已取得咗相當嘅成功,甚至有研究發現,用呢種程式做診斷仲準過要醫生做[44][45]。
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