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I've used deep learning for segmenting brain anatomical scans, and I worked in a lab that used neural networks to detect cancerous tumors. I suspect the first

I've used deep learning for segmenting brain anatomical scans, and I worked in a lab that used neural networks to detect cancerous tumors. I suspect the first major hospital-facing implementations of machine learning will be in radiology, e.g.: http://suzukilab.uchicago.edu/ http://suzukilab.uchicago.edu/, which has been diagnosing cancerous tumors in CT scans with neural networks since before it was cool (one reason you won't see the terms 'deep learning' in the literature since they were originally just 3-layer networks, before the term was even coined). IIRC it outperformed the average radiologist. I wonder if the label problem could be less difficult for some low-hanging fruit. The CT scan neural network required something like 40k labeled scans from a radiologist, but it could come for free: many yes/no disease detections will eventually be resolved by human labeling anyway by your doctor. If you had access, say, to every CT scan taken, and electronic health records for the patients, your labeling is noisy and biased but at least at massive scale. The problem is (legitimately) restricted access to health records in the US. Maybe some European countries have better data access? And the implementation problem will eventually disappear. I remember talking with a radiologist years ago, who remarked "some people in my field have no idea it's about to disappear". I'm not so sure there will be no more radiologists, but their role will definitely change. Hospitals would be okay with this, actually, since radiologists are expensive. Eventually radiology scans will probably be like ordering blood tests, where fewer and fewer MD's are required.

anonymousDan 10y ago
My Dad is a consultant radiologist, recently retired but still doing some part time work. When I've had conversations about whether he thinks his job might be automated, he says that the main problem he's seen with current ML systems is that they throw up far too many false positives. They also aren't great for unusual or corner cases. For example, in one scan he saw recently the very corner of the picture was occluded because the radiographer had left something on the machine (not quite sure what). He said most trainees would have just ignored it, but he sent it back. Turns out it was hiding a tumour. Now I know this is just one example, but he said you'd be surprised by the number of weird things that turn up like that. Where he does see it having a place is to help radiologists from missing really obvious things because they're tired. Most people don't realise how much concentration is required to just look at scan after scan for miniscule clues indicating a potential problem.
Toenex 10y ago
Many of the challenges with radiology are historical. Imaging technologies have been treated as a devices for producing images on which measurements can be made, rather than a measurement device from which images are formed. This has lead to difficulties for quantitative imaging techniques and so we continue to rely on the (albeit impressive) qualitative assessment of the radiologist. This is changing (shameless plug for my company http://www.pulmolux.co.uk http://www.pulmolux.co.uk) but medicine moves slowly preferring evolution over revolution. Having said that, I certainly detect that the scanner manufacturers (GE, Philips, Siemens, etc) having reached saturation with radiologists now have a thirst for disruption and see the referring physician as the next customer. MRI cardiology being something of an example.
naveen99 10y ago
Ct scans aren't really used to look for brain tumors. We use mri for that mostly. Ct is used for screening of stroke, trauma and other things. Source: radiologist / me. I work on radiology image segmentation also. And I agree it is solvable with machine learning. But even if software could do a job of a radiologist, it wouldn't replace one any more than your ekg reading program replaced cardiologists.
- kevinalexbrown 10y ago
  > Ct scans aren't really used to look for brain tumors. I can see how my phrasing was confusing, but I didn't mean to suggest that Ct scans are used to look for brain tumors. My work segmenting brain scans was not tumor-related, just gray/white matter segmentation.
- Toenex 10y ago
  > But even if software could do a job of a radiologist, it wouldn't replace one any more than your ekg reading program replaced cardiologists. I don't think the radiologist is going anywhere soon but the role is changing. Radiologists are increasingly having to deal with more and more derived information. They need to understand the algorithms being used as well as the biology, anatomy, physiology and disease being investigated. I can see a time when algorithmic specialists become a regular part of their multidisciplinary team.

anonymousDan 10y ago

My Dad is a consultant radiologist, recently retired but still doing some part time work. When I've had conversations about whether he thinks his job might be automated, he says that the main problem he's seen with current ML systems is that they throw up far too many false positives. They also aren't great for unusual or corner cases. For example, in one scan he saw recently the very corner of the picture was occluded because the radiographer had left something on the machine (not quite sure what). He said most trainees would have just ignored it, but he sent it back. Turns out it was hiding a tumour. Now I know this is just one example, but he said you'd be surprised by the number of weird things that turn up like that. Where he does see it having a place is to help radiologists from missing really obvious things because they're tired. Most people don't realise how much concentration is required to just look at scan after scan for miniscule clues indicating a potential problem.

Toenex 10y ago

Many of the challenges with radiology are historical. Imaging technologies have been treated as a devices for producing images on which measurements can be made, rather than a measurement device from which images are formed. This has lead to difficulties for quantitative imaging techniques and so we continue to rely on the (albeit impressive) qualitative assessment of the radiologist. This is changing (shameless plug for my company http://www.pulmolux.co.uk http://www.pulmolux.co.uk) but medicine moves slowly preferring evolution over revolution. Having said that, I certainly detect that the scanner manufacturers (GE, Philips, Siemens, etc) having reached saturation with radiologists now have a thirst for disruption and see the referring physician as the next customer. MRI cardiology being something of an example.

naveen99 10y ago

Ct scans aren't really used to look for brain tumors. We use mri for that mostly. Ct is used for screening of stroke, trauma and other things. Source: radiologist / me. I work on radiology image segmentation also. And I agree it is solvable with machine learning. But even if software could do a job of a radiologist, it wouldn't replace one any more than your ekg reading program replaced cardiologists.

kevinalexbrown 10y ago
> Ct scans aren't really used to look for brain tumors. I can see how my phrasing was confusing, but I didn't mean to suggest that Ct scans are used to look for brain tumors. My work segmenting brain scans was not tumor-related, just gray/white matter segmentation.
Toenex 10y ago
> But even if software could do a job of a radiologist, it wouldn't replace one any more than your ekg reading program replaced cardiologists. I don't think the radiologist is going anywhere soon but the role is changing. Radiologists are increasingly having to deal with more and more derived information. They need to understand the algorithms being used as well as the biology, anatomy, physiology and disease being investigated. I can see a time when algorithmic specialists become a regular part of their multidisciplinary team.