A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

Emma Beede, Elizabeth Baylor, Fred Hersch, Anna Iurchenko, Lauren Wilcox, Paisan Ruamviboonsuk, Laura M. Vardoulakis, A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy, CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, April 2020, Pages 1–12, https://doi.org/10.1145/3313831.3376718.

Referral Determinations

All images were initially assessed by a nurse then sent to an ophthalmologist for review. The ability to assess fundus photos for DR varied from nurse to nurse. While most nurses told us they felt comfortable assessing for the presence of DR, they didn’t know how to determine the severity if present. P4 told us, “I know if it’s not normal, but I don’t know what to call it.” To make the ultimate decision of whether a patient needs to be referred to an ophthalmologist for an exam and potentially for treatment, the nurse turned to the ophthalmologist or retinal specialist, who are most often remote.

NBDT: Neural-Backed Decision Trees

Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez, NBDT: Neural-Backed Decision Trees,  arXiv:2004.00221, 2020

We forgo this dilemma by creating Neural-Backed Decision Trees (NBDTs) that (1) achieve neural network accuracy and (2) require no architectural changes to a neural network. NBDTs achieve accuracy within 1% of the base neural network on CIFAR10, CIFAR100, TinyImageNet, using recently state-of-the-art WideResNet; and within 2% of EfficientNet on ImageNet. This yields state-of-the-art explainable models on ImageNet, with NBDTs improving the baseline by ~14% to 75.30% top-1 accuracy. Furthermore, we show interpretability of our model's decisions both qualitatively and quantitatively via a semi-automatic process. Code and pretrained NBDTs can be found at this https URL.

Digital Social Innovation (數位社會創新)

Audrey Tang (唐鳳), Digital Social Innovation (數位社會創新), 2020/4/21

Let Taiwan into the World Health Organisation

Economist, Let Taiwan into the World Health Organisation, 2020/03/26.

Spare a moment and admire Taiwan. Its handling of the new coronavirus pandemic has so far saved many, many lives. The figures tell the story. A country of 24m, it has far fewer infections than its neighbours: just 235 as of March 25th, with only two deaths...

原則：生活和工作 (Principles: Life and Work)

陳世杰、諶悠文、戴至中譯，原則：生活和工作，商業周刊，2018
Ray Dalio, Principles: Life and Work, Simon & Schuster, 2017 (excerpt)

 瑞．達利歐出身美國普通中產家庭，26歲時被投資公司炒魷魚，在自己的兩房公寓室白手起家創辦了橋水，並在接下來超過42年裡，把橋水打造成了獲《財星》（Fortune）雜誌評選為美國第五重要的私人公司。現在橋水管理資金超過1,500億美元，截至2015年年底，盈利超過450億美元。達利歐曾成功預測2008年金融危機，成為華爾街教父級大神。 

一路以來，達利歐曾入選世界百大最具影響力人物［《時代》（Time）］與百大富豪［《富比世》（Forbes）］，而且由於他獨特的投資原則改變了業界，《CIO》更稱他是「投資界的史蒂夫．賈伯斯」。 

他是怎麼辦到的？靠的是「原則」！他從1982年看錯墨西哥債務危機、狠狠跌交的經驗中吸取教訓，提煉決策標準，日積月累，總結成一組「原則」，包含21條高層原則、139條中原則和365條分原則，涵蓋為人處事、公司管理兩大方面，是橋水的員工手冊，橋水依循進行日常管理，也是橋水成為全球最強避險基金的祕密。