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We believe that AI might advance medical care by improving efficiency of triage
to subspecialists and the personalisation of medicine through tailored prediction
models. The automated approach to prediction model design improves access to this
technology, thus facilitating engagement by the medical community and providing
a medium through which clinicians can enhance their understanding of the
advantages and potential pitfalls of AI integration."
In this case Cloud AutoML Vision has been used. So, let's look at an example.
Chapter 14
Using Cloud AutoML ‒ Vision solution
For this example, we are going to use the code made by Ekaba Bisong and available
as open source under the MIT License (https://github.com/dvdbisong/automlmedical-image-classification/blob/master/LICENSE).
Here the task is to
classify images:
Figure 23: Lung chest X-rays
This type of classification requires expert knowledge when performed by humans.
Using language typical of clinicians who are specialized in analyzing chest
X-rays: "The normal chest X-ray (left panel) shows clear lungs with no areas of
abnormal opacification. Bacterial pneumonia (middle) typically exhibits a focal
lobar consolidation, in this case in the right upper lobe (see arrows), whereas viral
pneumonia (right) manifests with a more diffuse "interstitial" pattern in both lungs".
(Source: Kermany, D. S., Goldbaum M., et al. 2018. Identifying Medical Diagnoses
and Treatable Diseases by Image-Based Deep Learning. Cell. https://www.cell.
com/cell/fulltext/S0092-8674(18)30154-5)
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