Deep Learning Transforms Smartphone Microscopes into Laboratory-Grade Devices深度學習將智能手機顯微鏡改造成實驗室級設備

Researchers at the UCLA Samueli School of Engineering have demonstrated that deep learning， a powerful form of artificial intelligence， can discern and enhance microscopic details in photos taken by smartphones. The technique improves the resolution and color details of smartphone images so much that they approach the quality of images from laboratory-grade microscopes.

加州大學洛杉磯分校（UCLA）薩繆爾工程學院的研究人員展示了“深度學習”這一強大的人工智能技術，它能辨識并增強智能手機所攝照片的微觀細節。這項技術可以顯著提升智能手機圖像的分辨率和色彩細節，使圖像質量逼近實驗室級顯微鏡所攝圖像。

The advance could help bring high-quality medical diagnostics into resource-poor regions， where people otherwise do not have access to high-end diagnostic technologies. And the technique uses attachments that can be inexpensively produced with a 3-D printer， at less than $100 a piece， versus the thousands of dollars it would cost to buy laboratory-grade equipment that produces images of similar quality.

這一進步可能助推高質量醫療診斷進入資源匱乏的地區，那些地區的居民目前享受不到高端診斷技術帶來的好處。而且，這項技術使用的附件可以通過3D打印機生產，價格低廉，每件成本不足100美元。相比之下，購置能生成具有類似質量圖像的實驗室級設備需要花費數千美元。

Cameras on today’s smartphones are designed to photograph people and scenery， not to produce high-resolution microscopic images. So the researchers developed an attachment that can be placed over the smartphone lens to increase the resolution and the visibility of tiny details of the images they take， down to a scale of approximately one millionth of a meter.

如今，智能手機上的攝像頭主要用于拍攝人物和風景，而不是生成高分辨率的顯微圖像。因此，研究人員研制出一款可安裝在智能手機鏡頭上的附件，能提高分辨率，提升所攝圖像中纖微細節的可視程度，精度近乎百萬分之一米。

But that only solved part of the challenge， because no attachment would be enough to compensate for the difference in quality between smartphone cameras’ image sensors and lenses and those of high-end lab equipment. The new technique compensates for the difference by using artificial intelligence to reproduce the level of resolution and color details needed for a laboratory analysis.

但這僅僅解決了部分難題，因為任何附件都無法完全彌補智能手機攝像頭的圖像傳感器和鏡頭與高端實驗室設備之間在質量方面的差異。新技術通過人工智能重現實驗室分析所需的分辨率和色彩細節，彌補了這一差距。

The research was led by Aydogan Ozcan， Chancellor’s Professor of Electrical and Computer Engineering and Bioengineering， and Yair Rivenson， a UCLA postdoctoral scholar. Ozcan’s research group has introduced several innovations in mobile microscopy and sensing.

這項研究是由UCLA電氣與計算機工程及生物工程專業的校長教授埃道甘·奧茲坎和博士后學者亞伊爾·里文森主持。奧茲坎的研究團隊在移動顯微鏡和傳感技術領域取得了多項創新成果。

“Using deep learning， we set out to bridge the gap in image quality between inexpensive mobile phone-based microscopes and gold-standard bench-top microscopes that use high-end lenses，” Ozcan said.

奧茲坎表示：“借助深度學習技術，我們可以著手消除低價位手機顯微鏡和配備高端鏡頭的頂級臺式顯微鏡之間的圖像質量差距。”

“We believe that our approach is broadly applicable to other low-cost microscopy systems that use， for example， inexpensive lenses or cameras， and could facilitate the replacement of high-end bench-top microscopes with cost-effective， mobile alternatives.”

“我們認為，此方法可廣泛應用于其他低成本顯微鏡系統，比如配備廉價鏡頭或攝像頭的系統，并且能夠推動性價比高的移動顯微鏡替代高端臺式顯微鏡。”

He added that the new technique could find numerous applications in global health， telemedicine and diagnostics-related applications.

他還補充道，這項新技術可以在全球健康、遠程醫療及與診斷相關的應用程序中得到廣泛運用。

The researchers shot images of lung tissue samples， blood and Pap smears， first using a standard laboratory-grade microscope， and then with a smartphone with the 3D-printed microscope attachment. The researchers then fed the pairs of corresponding images into a computer system that “learns” how to rapidly enhance the mobile phone images.

研究人員分兩次采集了肺組織樣本、血液和巴氏涂片的影像：首先使用的是標準的實驗室級顯微鏡，然后使用了搭載3D打印顯微鏡附件的智能手機。接著，研究人員將對應圖像成對輸入計算機系統，讓系統“學習”如何快速提升手機拍攝所得的圖像質量。

To see if their technique would work on other types of lower-quality images， the researchers used deep learning to successfully perform similar transformations with images that had lost some detail because they were compressed for either faster transmission over a computer network or more efficient storage.

為檢驗該技術是否適用于其他低質量圖像，研究人員運用深度學習算法，對一些因壓縮導致部分細節缺失的圖像成功地進行了類似處理。壓縮圖片是為了實現電腦網絡上的更快傳輸或更高效存儲。

The study was published in ACS Photonics， a journal of the American Chemical Society.

該項研究的報告發表在美國化學學會期刊《ACS光子學》上。

（譯者單位：河北科技大學）