Learn English with the News – The Pastry AI that Learned to Fight Cancer

Science and food do not have to be mutually exclusive: cancer researchers in Japan have been working with software developers to adapt an innovative computer program that can identify hundreds of different types of pastries at the cash register into a program that can detect cancer cells under a microscope lens.

Watch the video and then do the accompanying English language exercises.

The news is a consistent source of entertainment, knowledge and discovery that never ceases to exist and always comes out with more and more material each day. Because it plays such a vital part in our lives and is so important to keep up with, it is without a doubt a piece of your everyday routine that can’t go ignored.

Whether it is to understand the ramifications of recent legislation passed, to hear about recent events and grasp the potential consequences to your country, or simply hear about what is happening in other countries in order to compare them to what’s happening in yours, the news is certainly a staple in our lives and the most consistent way to get information.

This is why Scrambled Eggs has decided to unite two of your biggest worlds: learning English and keeping up with what is happening in the world. We hope our challenging daily exercises, composed of listening, vocabulary and comprehension exercises in English, will satisfy both of those above worlds in a satisfactory and also entertaining way.

So enough about introductions, let’s get to today’s Learn English with the News topic:

Adapted from this article. 

The Pastry AI that Learned to Fight Cancer | Fill in the Blank

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The Pastry AI that Learned to Fight Cancer | Synonyms Match

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The Pastry AI that Learned to Fight Cancer | True or False

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Full text:

“A software company called Brain has been working with a cancer research center in Kyoto, Japan to adapt software they created for the Japanese bakeries into a program that can detect cancer cells under a microscope lens.

Brain’s software, BakeryScan, was created in 2007 and has since been improved to allow Japan’s bakeries to easily identify different types of pastries at the cash register.

The pastry industry needed this complex software because of Japan’s very diverse food tastes. The country’s long trade history led to its desire for a variety of flavors. For this reason, unlike French or Italian bakeries that offer only a few options, Japanese bakeries offer pastries of all sizes, shapes, flavors, and colors. There are hundreds of different types of pastries in these unique bakeries.

The many different types of pastries caused cashiers to spend months learning the price of each individual pastry based on sight alone. This meant that the checkout process was not only very difficult for cashiers, but also caused long wait times for customers.

Brain, which was founded by computer programmer and software designer Hisashi Kambe, had always worked on projects based on computer visualization capabilities and so to combat this problem at the cash register they created BakeryScan.

BakeryScan is unique because, unlike deep learning software like Google Translate, Siri, and almost every AI system out there, it doesn’t need large amounts of specialized data to make decisions; it is created to understand irregularities like the shadow cast into the middle of a donut hole or the slightly darker color of over-baked bread without needing the input of tens of thousands of similar images.

When a doctor at the Louis Pasteur Center for Medical Research, in Kyoto, saw a television segment about the machine, he realized that cancer cells, under a microscope, looked a lot like bread. He contacted Hisashi Kambe’s company Brain to see how they could collaborate to develop a version of the program that could help pathologists detect cancer cells.

The program they came up with, Cyto-Aiscan, is currently being tested in two major hospitals in Kobe and Kyoto. It is capable of “whole-slide” analysis, meaning that it can analyze an entire microscope slide and identify the cells that might be cancerous. The software considers the color tone of the nucleus, its size and texture, and its overall roundness and can lead to earlier diagnoses by speeding up the process, ultimately allowing for more effective treatments for cancer patients.”

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