Applications of #DeepLearning in #Healthcare
Posted on March 28th, 2017
03/28/2017 @NYU Courant Institute (251 Mercer St, New York, NY)
Sumit Chopra, the head of A.I. Research @Imagen Technologies, introduced the topic by saying that the two areas in our lives that will be most affect by AI are healthcare and driverless cars.
Healthcare data can be divided into
- Other – cell phones, etc.
Payer data – from insurance provider
Clinical data – incomplete since hospitals don’t share their datasets; digital form with privacy concerns
Payer data more complete unless the patient switches the payer, less detail.
He focuses on medical imaging – mainly diagnostic radiology – 600mm studies in the U.S., but shortage of skilled radiologists. Prevalence of errors. The images are very large size, high resolution, low contrast, highly subtle cues => radiology is hard to do well
Possible solution: pre-train a standard model: Alexnet/VGG/… on a small number of images, but this might not work since the signal is subtle.
Also radiology reports, which could be used for supervised training, are unstructured and it’s hard to tell what the report tells you. => weak labels at best
Much work has been done on this problem, usually using deep convolutional neural nets.
First step: image registration = rotate & crop.
Train a deep convolutional network (registration network) , the send to a detection network for binary segmentation.
Could use generative models for images to train doctors
Leverage different modalities of data
Sumit has round that a random search of hyperparameter space works better than either grid search or optimizer search.