
AI Helps Classify Lung Cancer at the Pathologist Level - ml-engineer
https://news.developer.nvidia.com/ai-helps-classify-lung-cancer-at-the-pathologist-level/
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DTE
Very cool! We (Paperspace) just posted a tutorial on Detecting Pneumonia from
X-ray scans using PyTorch for anyone interested in building some more
intuitions around this type of Deep Learning:
[https://blog.paperspace.com/detecting-and-localizing-
pneumon...](https://blog.paperspace.com/detecting-and-localizing-pneumonia-
from-chest-x-ray-scans-with-pytorch/)

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toomuchtodo
Is this in clinical use anywhere yet?

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jacquesm
Yes, there is a whole glut of companies in this space working in close
collaboration with various hospitals. I've looked at several of these and the
general quality of the products is high.

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rhave
Sounds nice, and they even have the good stuff on github with an explaining
how-to article on medium.

If you're into Digital Pathology and Whole Slide Image (WSI) analysis then
there is a whole undergrowth of open source tools and projects to dive into. I
especially like QuPath by Pete Bankhead
([https://qupath.github.io/](https://qupath.github.io/)). It has a more
accessible interface than python (as in the article), it supports various
machine learning algorithms and also has some nice video tutorials
([https://www.youtube.com/channel/UCk5fn7cjMZFsQKKdy-
YWOFQ](https://www.youtube.com/channel/UCk5fn7cjMZFsQKKdy-YWOFQ)).

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jszymborski
I worked on this problem during my masters. I created both a deep and KNN
model for early breast cancer detection, but I think the performance (accuracy
and speed) is most impressive for the latter. Code:
[https://github.com/jszym/pprecogg](https://github.com/jszym/pprecogg)

Thesis:
[https://www.cs.mcgill.ca/~jszymb/thesis/260528685_Szymborski...](https://www.cs.mcgill.ca/~jszymb/thesis/260528685_Szymborski_Joseph_Experimental_Medicine_thesis.pdf)

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sgt101
143 images; and within 95%. I need to read more to work out which 5% is wrong,
and what the 95% really means of course. I feel that calling this Pathologist
level could be a stronger claim that I would make. I am hesitant to word
things more strongly, but I feel dismay about the kind of claims of
performance that are often made in these contexts.

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csours
One of my college instructors talked about this back in 2005 - did it really
take this long for the tech to mature?

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kendallpark
One possible reason: pathology labeling can be prohibitively expensive. You're
looking at paying highly-trained specialists to sit down and hand-segment
regions of cell types on an entire slide.

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jacquesm
That is definitely one bottle-neck, creating the labelled data-sets is very
expensive, besides that there are a large number of privacy sensitive issues
that need to be taken care of around the whole labeling process to ensure that
patient confidentiality is maintained. Large datasets exist but in general are
not available for research for that reason, only a few hospitals have made
datasets available without restriction.

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blackrock
What markers does a human Pathologist look for to detect Lung Cancer?

Tumors? Dark spots?

And on what type of media? Xrays? MRIs?

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kgwxd
Al does it again! Where does he find the time?

