Breast cancer detection in mammography using deep learning approach

There are several key points that get left out in AI radiology conversations such as this one:<p>1) Mammograms are not interpreted in a vacuum. In fact mammograms are usually the first in a long line of tests before a breast cancer or other diagnosis is ultimately made. In fact, it&#x27;s probably more accurate to refer to mammography as a screening exam for which patients need a biopsy rather than a diagnostic test for cancer (there are rare exceptions, but overall this point holds).<p>2) Speaking frankly as a radiologist myself, tests like mammograms aren&#x27;t even that good in terms of overall diagnosis. Thats why ultrasound, tomosynthesis and MRI are often used as supporting evidence and&#x2F;or alternative exams.<p>3) There is controversy over the overall utility of mammograms, particularly in the screening context. Radiologists more than anyone would like the sensitivity and specificity of these studies to be higher.<p>It strikes me that the people that push these &quot;radiology is ripe for disruption&quot; or &quot;AI outperforms radiologists&quot; hyperbolic arguments are clearly people that have never seen the inside of a clinic. I&#x27;m sure they love this rhetoric though when pitching to VCs or sitting around the conference table coming up with &#x27;breakthrough ideas&#x27; to turn into power-points for the other administrators.

Of the major specialties, it seems that radiology is the most in danger of significant disruption. First of all, it can be done remotely, so there is risk if the regulation is lightened that foreign radiologists will be allowed to read studies at much less cost. The other issue is that this is something that deep learning can rapidly progress in given there are already a plethora of labeled data sets. For example, every mammogram that is taken has already been labeled normal or abnormal.

I imagine this is just scratching the surface and before long we&#x27;ll be doing full-body, 3D scans every few years and everything from cancers (all of them) to heart disease, to gastero-intestinal issues, to things even as mundane as acne and dandruff will be diagnosed by algorithms pulling on a cumulative database of images of healthy and diseased body parts. The real hope is to be able to see into the brain and pick up things like CTE and Alzheimers <i></i>years<i></i> before symptoms manifest.

Instead of trying to squeeze blood from the mammography stone that has failed to improve longevity in breast cancer patients despite enormous investment over many years, AI&#x2F;ML need to take a broader perspective and look at modalities like circulating tumor DNA and 3-D ultrasound

As the authors rightly call out in the abstract <i>obtaining large amounts of annotated data poses a challenge for training deep learning models for this purpose</i> But doesn&#x27;t appear that the data they&#x27;ve collected and annotated is made available from my read of the paper, I get that this is from a company (DeepHealth) but it seems like an opportunity for NIH to push for more broadly available data sets.<p>Anyone have a good reference point for the reader selection of 5 specialists with 5.6yrs avg experience? That population seems small. Another opportunity for licensing bodies or national institutions to grow a publicly available dataset -- including annotations from a wider selection of imaging specialists.

I see a lot of machine learning work on medical imagery, which is great, but it seems like this is solving a problem that the human brain is already pretty good at (image recognition). I wish I saw more work being done on finding patterns in medical data in numeric formats which the human brain is terrible at. Is there much of that going on?

Got me thinking... How long till AWS DeepPhysician? They don&#x27;t seem to have a clear cut on the limit of the scope of their services. Joke apart, what would be the implications and responsibilities of big tech entering the medical field?

Excellent results with good generalization. The study appears to be well designed and executed. This was a significant effort. Clearly, there are commercial intentions.

Computer assisted screening for mammo has been around for years... like icad I am sure some of the vendors in this space are using some form of deep learning already

Everyone swooning with optimism over this result should the machine learning reddit comments on it first.<p><a href="https:&#x2F;&#x2F;old.reddit.com&#x2F;r&#x2F;MachineLearning&#x2F;comments&#x2F;ehpllt&#x2F;deep_learning_model_for_breast_cancer_detection&#x2F;" rel="nofollow">https:&#x2F;&#x2F;old.reddit.com&#x2F;r&#x2F;MachineLearning&#x2F;comments&#x2F;ehpllt&#x2F;dee...</a><p>And also linked blog[edit]:<p><a href="https:&#x2F;&#x2F;lukeoakdenrayner.wordpress.com&#x2F;2017&#x2F;12&#x2F;06&#x2F;do-machines-actually-beat-doctors-roc-curves-and-performance-metrics&#x2F;" rel="nofollow">https:&#x2F;&#x2F;lukeoakdenrayner.wordpress.com&#x2F;2017&#x2F;12&#x2F;06&#x2F;do-machine...</a><p>TL;DR; There are sooo many subtlties to stuff like this that this things really shouldn&#x27;t be taken at face value. This is far from replacing doctors in anything.

This is simply amazing. Great job to anyone involved in that project!

Dr Sausage has had this technology for decades

If you find this kind work interesting, our AI group at Siemens Healthineers is hiring interns to carry out projects like this. We typically target machine learning or medical imaging PhD students, but are open to a variety of backgrounds. Please feel free to reach out via email.

The Clincal Center for Data Science at Massachusetts General Hospital (one of the top hospitals in the world) is hiring for a variety of positions. We have access to tons of medical data (imaging, NLP, time series), clinical domain expertise, and one of the largest GPU computing clusters. <a href="https:&#x2F;&#x2F;www.ccds.io&#x2F;careers&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.ccds.io&#x2F;careers&#x2F;</a>

It will be a great day for individuals everywhere when we automate away 75% of MD&#x2F;DO jobs. It won&#x27;t be a great day for the AAMC, and I also look forward to seeing how they respond.

And as my MD will tell you, voodoo! No one can ever replace me. No &quot;robot.&quot; :-)