Science of Machine Learning (was Machine Learning , some thoughts)

Bert Verhees bert.verhees at rosa.nl
Tue Jul 3 08:12:23 EDT 2018


On 03-07-18 12:21, Philippe Ameline wrote:
> Le 02/07/2018 à 11:31, Bert Verhees a écrit :
>
>> On 30-06-18 17:16, Philippe Ameline wrote:
>>> (improperly labeling images or adding images of objects that are not
>>> plants) could probably make the whole app plainly crappy.
>> Of course Philippe, but that would be vandalism. Most sensible people
>> don't do that when they stand behind the goal, and a little bit of
>> dirt, therefor it is Machine Learning, it can filter it out. It is
>> part of the learning process.
> If a culture of data quality is properly installed, then it is possible
> to name improper use "vandalism".
> In medicine, since such a culture has never existed, we could name it
> "don't carisme", "no time for thisisme" or "was never thaughtisme".
Okay, not vandalism but don't-careism. The result is different. The 
first gives wrong data to frustrate the machine learning process, the 
second does not give data, voluntarily or not of good quality.

Good that there are procedures that create good data to learn from, 
these data are recorded anyway.
For example, in medical imaging diagnosis. Often this is very accurate 
and also cheap and fast. This not science fiction. This not new.
Early detection of diseases can reduce cost for healthcare enormously 
and will change the daily practice of healthcare.

Not only to find cancer, but even early detection of alzheimer is being 
worked on or already in use.
Currently, medical images account for 90% of all medical data, according 
to an IBM-report a year ago. This will be much more, and this will 
happen soon.

These machine learning processes do not suffer from don't-careism 
because the images are produced anyway, and have the manual diagnosis to 
learn from also.
Medical imaging is a good candidate for machine learning, not only 
because of the data which are very suitable, but also because of the 
importance, and (I repeat because of your argument) the processing for 
getting data does not require extra effort.

Upload images to a web-service, so hospitals do not have to buy 
expensive machines or employ specialists for this. Just upload the image 
and within 5 seconds, there is an analysis with high accuracy and cheap.
https://lunit.io/
https://www.vuno.co/

Also ultrasound supported by machine-learning/deep learning, “Users can 
reduce taking unnecessary biopsies and doctors-in-training will likely 
have more reliable support in accurately detecting malignant and 
suspicious lesions,” said Professor Han Boo Kyung, a radiologist at 
Samsung Medical Center.
https://www.samsunghealthcare.com/en/products/UltrasoundSystem/RS85/Radiology/benefit

I think it is time for optimism. But there is one thing that can come in 
the way. People might not accept being diagnosed by a machine, although 
this diagnose is more trustable. Also radiologist may fear for their 
employment, but instead of taking radiologists’ jobs, their job will 
change to prediction and guiding treatment. (so says Dr. Bradley 
Erickson from the Mayo Clinic in Rochester, Minnesota)

Bert





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