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Breast cancer detection in mammography using deep learning approach
- oarabbus_ 7y agoIt will be a great day for individuals everywhere when we automate away 75% of MD/DO jobs. It won't be a great day for the AAMC, and I also look forward to seeing how they respond.
- MiroF 7y agoThe same way they already respond to the fact that nurses/NP are capable of doing a large number of jobs reserved for MDs: lobby and regulate.
- oarabbus_ 7y agoI also can't wait for the day that the regulations you refer to which prevent NPs from doing jobs they are fully qualified for (and thereby improving the medical care for the nation) are struck down. I'm not sure that day will ever come.
- natalyarostova 7y agoThe cotton gin increased demand for labor to use the tool. You have to think very carefully and subtly to even have a chance at predicting second order effects from more complex forms of automation.
- jcims 7y agoSame is true of regulation.
- oarabbus_ 7y agoI worked for a surgical robotics company as well as other medical device companies, and procedure lengths and surgical wait times both decreased in facilities with the device. Based on my experience the danger is in areas like "will we over-prescribe imaging tests even more than now?" but as to whether we'd need less doctors to treat the same number of patients, the answer is yes.
- natalyarostova 7y agoIf imaging tests become higher quality and cheaper would the concept of over-prescription of them still make sense? (Asking sincerely, I don’t work in medicine)
- oarabbus_ 7y agoIt is still dangerous, as x-rays are carcinogenic, and also the chance of false positives can adversely affect patient health.
- catalogia 7y agoFrom what I understand the increase in demand for labor was not to use the tool itself (which I believe would generally be water powered, not hand cranked) but rather the tool induced a greater demand for raw cotton and provided expansion opportunities to industries that consumed cotton. So more people were picking cotton and working in textile factories.
- Gatsky 7y agoI'm interested to know why you have this negative view of medicine. I'm also interested in why you think the alternate future where medicine is automated (assuming that is possible and/or desirable to most people) is likely to be better for society as a whole.
- mscasts 7y agoThis is simply amazing. Great job to anyone involved in that project!
- yellow_postit 7y agoAs the authors rightly call out in the abstract obtaining large amounts of annotated data poses a challenge for training deep learning models for this purpose But doesn't appear that the data they'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. 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.
- melan13 7y agoMammogram Images are far away to present any privacy challenge especially if identities are not disclosed.
- 1e-9 7y agoThe radiologists in this study had read 6,969 mammograms on average over the preceding year. That's about 15X the certification requirement and 4X the average for U.S. doctors. Reading volume is one of the main predictors of performance[1], which suggests that these doctors were probably above-average readers. It would have been nice to see more readers involved, but reader studies are a major effort. Even with the small sample size of readers, these results were statistically significant as well as clinically significant. [1] https://www.ncbi.nlm.nih.gov/pubmed/21343539 https://www.ncbi.nlm.nih.gov/pubmed/21343539
- RcouF1uZ4gsC 7y agoOf 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.
- mikece 7y agoIt's also an area where a scan from a year ago can be re-processed based on new research and find something that wasn't detected on the first processing run -- and still be found years earlier than current methods. Lots of promise here. As for disrupting the field, practicing radiology will become a rare job for humans but the demand for basic research in radiology will likely go through the roof so that the AI can be properly improved and expanded.
- aswanson 7y agoTalk to some lawyers. Not happening anytime soon.
- chance_state 7y agoCare to elaborate?
- chongli 7y agoFalse positives are a major source of morbidity in cancer treatment. Biopsies and unnecessary major surgeries are a big problem.
- nopinsight 7y agoWhat if human doctors double check results for false positives? They will help prevent unnecessary procedures. The algorithms would still allow scans to be read more efficiently since negative results for low-risk patients can be mostly automated away.
- mikece 7y agoI imagine this is just scratching the surface and before long we'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 years before symptoms manifest.
- 3fe9a03ccd14ca5 7y agoI think I’ll pass on being a beta tester. Sounds like there’s a lot of risk from unnecessary intervention.
- jabits 7y agoOnly if you act on it. Just because you know or suspect something doesn’t mean you have to do something about it.
- jcims 7y agoMy wife had multiple softball sized tumors growing in her abdomen for months/years that would have easily been detected by something like this. Instead they burst and metastasized and now we're a million dollars deep into medical treatments and still nothing remotely resembling a guarantee of resolution. You can always choose not to act on what you learn. You can't act on what you don't know.
- catalogia 7y ago> You can always choose not to act on what you learn. You can't act on what you don't know. You can also act on what you think you know but in fact don't. False negatives kill people but so can false positives.
- jcims 7y agoWhat are the numbers though? Having unnecessary procedures done because of false positives from a screening test can absolutely kill people, but so can wearing a seatbelt. People drown and burn up all the time because they couldn't get out of a wrecked vehicle. There is presently some set of screening tests with varying levels of sensitivity and specificity, and they aren't all appropriate for mass screening. However, if millions of people started regularly getting non-ionizing imaging done through MRI or ultrasound or infrared or whatever, we would learn a shitload about predicting maladies and likely save quite a few more people than we kill in the process.
- travisoneill1 7y agoI 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?
- toomuchtodo 7y agoThe human brain could always use a bit of help. Think of these sorts of tools as medical IDEs for practitioners. Anything that reduces costs, improves outcomes, or both, are welcome.
- jcims 7y agoYou're absolutely correct but from what I can see there's going to need to be a lot more work done in collecting and standardizing that data before it's available in sufficient quantity and quality to do anything with. I think a more aggressive approach to normalizing externalities (primarily regulated diet/nutrition) would help as well. There's another completely (to me) unintuitive angle as well. Andrew Lo and some folks from MIT Sloan have published a paper about using clinical trial data to predict which medicines will be approved by the FDA in order to help reduce investment risk and unlock dollars. He does a pretty good talk about it here - https://www.youtube.com/watch?v=AzELyaVf0v8 https://www.youtube.com/watch?v=AzELyaVf0v8 He's on a recent episode of Linear Digressions discussing this as well. http://lineardigressions.com/episodes/2019/12/8/using-data-science-to-make-hard-prioritization-decisions-behind-the-scenes-of-an-analysis-to-predict-drug-approvals http://lineardigressions.com/episodes/2019/12/8/using-data-s...
- deleted 7y ago[deleted]
- anthony_doan 7y ago> Is there much of that going on? Most of that are statistical methods in statistic and not ML. We're talking about survival analysis, longitudinal analysis, clinical trial, nonparametric statistic, etc... From my experiences ML is too dependent on large dataset. Medical data are often high dimensional and small. My thesis papers have leverage two statistician works to make decision trees and ensemble leverage more statistic to handle medical data (high dimensional data). As noted by Dr. Harrell, statistician, ML is much more suited with less noisy data, medical image. Also inference is most more important in the medical field that just prediction.
- kevinalexbrown 7y agoIf 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.
- selimthegrim 7y agoPhysics PhDs?
- 256lie 7y agoThe 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. https://www.ccds.io/careers/ https://www.ccds.io/careers/
- abrichr 7y agoI'm curious, how is the compensation, e.g. compared to the lowest levels at https://www.levels.fyi/ https://www.levels.fyi/ ?
- joe_the_user 7y agoEveryone swooning with optimism over this result should the machine learning reddit comments on it first. https://old.reddit.com/r/MachineLearning/comments/ehpllt/deep_learning_model_for_breast_cancer_detection/ https://old.reddit.com/r/MachineLearning/comments/ehpllt/dee... And also linked blog[edit]: https://lukeoakdenrayner.wordpress.com/2017/12/06/do-machines-actually-beat-doctors-roc-curves-and-performance-metrics/ https://lukeoakdenrayner.wordpress.com/2017/12/06/do-machine... TL;DR; There are sooo many subtlties to stuff like this that this things really shouldn't be taken at face value. This is far from replacing doctors in anything.
- Gatsky 7y agoThose criticisms aside, if this system went up against the current status quo in a prospective study, there is a reasonable chance it would be on par at least with humans. Whether that makes it a worthwhile endeavour comes down to questions of cost, technical complexity and health outcomes. This last part is actually a significant barrier to 'AI' in healthcare. For that reason, I suspect most companies will prefer to sell their products integrated into assistant style software, where the value proposition is tied to reimbursement eg reporting more scans. The sensitivity is also still not as high as you would like ideally... this is a limitation of mammography.
- 1e-9 7y ago1) I see no credible criticism on reddit. 2) I see nothing in the Luke Oakden-Rayner blog that calls this study into question. This study actually avoids the pitfalls that he mentions. 3) This paper said nothing about replacing doctors.
- statesdj 7y agoInstead 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/ML need to take a broader perspective and look at modalities like circulating tumor DNA and 3-D ultrasound
- avocado4 7y agoWhy those two in particular? Especially 3D US - so far it's mostly been used for "cosmetic" purposes of getting a 3D picture of your baby. There hasn't been much clinical evidence to its usefulness from what I can tell.
- brooklyndude 7y agoAnd as my MD will tell you, voodoo! No one can ever replace me. No "robot." :-)
- jaxr 7y agoGot me thinking... How long till AWS DeepPhysician? They don'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?
- sergers 7y agoComputer 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
- neuro_image3 7y agoThere are several key points that get left out in AI radiology conversations such as this one: 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'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). 2) Speaking frankly as a radiologist myself, tests like mammograms aren't even that good in terms of overall diagnosis. Thats why ultrasound, tomosynthesis and MRI are often used as supporting evidence and/or alternative exams. 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. It strikes me that the people that push these "radiology is ripe for disruption" or "AI outperforms radiologists" hyperbolic arguments are clearly people that have never seen the inside of a clinic. I'm sure they love this rhetoric though when pitching to VCs or sitting around the conference table coming up with 'breakthrough ideas' to turn into power-points for the other administrators.
- ggm 7y agoI wish there was a way to repost this to all most every medical breakthrough ml story.
- cameronfraser 7y agoIt could be useful as a tool to help a radiologist do their job better though. I think many of these techniques described in ML papers will be used to enable people to be better at their jobs rather than replace them. At least until there is AGI at least.
- neuro_image3 7y agoI wouldn't dispute this (if they finally put something together that isn't horrendously cumbersome, time-consuming and hard to use) but this doesn't justify the 'AI is about to replace radiology' crap I seam to see every time some academic group publishes an AI/ML paper.
- 1e-9 7y agoExcellent results with good generalization. The study appears to be well designed and executed. This was a significant effort. Clearly, there are commercial intentions.
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- rogerdickey 7y agoDr Sausage has had this technology for decades
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- dsiarri 7y agoThis is a worth wild read.
- awayfromhomenow 7y agoI was IT for a company back in the late 90's that was using neural networks (along with a non-invasive sensor net)to detect breast cancer. Didn't quite work then, would be nice to see it revived with newer tech.
- RuthReid102 7y agoI was diagnosed with locally advanced breast cancer in May 2017, I had 2 huge tumors measuring nearly 10 cms in total. The cancer had moved to my skin and all my lymph nodes above and below my collarbone.I was inoperable so started 6 months of chemo. The side effects were devastating. I don’t think I shall ever recover from that, mentally or physically.Then I have a mastectomy a month after chemo finishes, to give me a rest.The mastectomy reveals that I need an auxiliary node clearance so have another operation another month after that. I was really confused because the pain was more than i can bear and was thinking that was the end of me. A cousin of mine introduced madida herbal breast cancer formula to me and i used it as instructed and believe me it worked so well, fast and great without any side effect. i went for examination few months later because i was feeling better with zero pain and symptoms i used to experience and my result came out Negative of Breast Cancer I used to have, i felt so happy and alive again. I read more about them on their website at www.madidaherbalclinic.weebly.com and they are specialized in curing all kind of diseases. You can email them directly at madidaherbalcenter@gmail.com to place an order, they really saved my life.