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At first glance, astronomers and cancer researchers may not seem to have a lot in common.
乍一看，天文學(xué)家和癌癥研究者似乎沒有什么共同點。
After all, astronomers spend their time gazing at distant stars and galaxies, while oncologists fight tumors.
畢竟，天文學(xué)家致力于觀察遙遠的星體和星系，而腫瘤學(xué)家則治療腫瘤。
But at the University of Cambridge in the UK, astronomers and cancer researchers have joined forces.
但是，在英國的劍橋大學(xué)，天文學(xué)家和癌癥研究者已經(jīng)開始合作了。
Specifically, astronomers there repurposed image analysis algorithms they use to spot distant galaxies to help oncologists diagnose aggressive tumors.
具體來說，天文學(xué)家改進了過去曾用于觀測遙遠星系的影像分析法，來幫助腫瘤學(xué)家診斷惡性腫瘤。
Here is how it works
如何實現(xiàn)
Astronomers use the algorithms to analyze faint, indistinct images captured by large telescopes.
天文學(xué)家用這種影像分析方法分析由大型望遠鏡捕捉到的微弱，模糊的星體圖像。
Powerful computers help identify these images as galaxies, stars, planets, and other celestial bodies.
強大的計算機可以幫助辨認這些圖像是星系，恒星，行星還是其它天體。
When cancer researchers study tumors, they stain samples to pick out subtle differences that clue them in to what types of proteins a given tumor harbors.
當(dāng)癌癥研究者研究腫瘤時，對樣本染色，進而辨別出微小的差異，染色可以顯示出腫瘤樣本的蛋白質(zhì)類型。
Identifying the proteins helps researchers know if a tumor is especially aggressive.
鑒別蛋白質(zhì)可以幫助研究人員確定腫瘤是否是惡性的。
This process typically involves individual scientists peering at tumor samples under microscopes, which is time consuming and laborious.
這一過程通常需要專業(yè)的科學(xué)家在顯微鏡下觀察腫瘤樣本，既費時又費力。
But using reconfigured image analysis algorithms to identify tumor proteins can speed the process up significantly.
但是使用改良后的圖像分析方法鑒別腫瘤蛋白質(zhì)，速度就顯著加快了。
To test the process, the researchers used the adapted algorithms on samples from more than 2,000 breast cancer patients.
為了測試這種方法，研究人員對2000多名乳腺癌患者的樣本使用改良后的算法分析。
They found that computer analysis was just as accurate at detecting specific proteins as scientists peering through microscopes.
他們發(fā)現(xiàn)，電腦分析和科學(xué)家通過顯微鏡的觀察在鑒別特定蛋白質(zhì)時同樣精確。
And the computer was much faster.
而且計算機的速度更快。
The researchers plan to further refine the computerized method by testing it on samples from more than 20,000 breast cancer patients.
研究人員計劃通過測試20000多名乳腺癌患者樣本，進一步改進計算機算法。
