We’ve just made public 125,091 DICOM radiology images from across my cancer journey (link below). The data is both longitudinal and multi-modal, dated and linked to my treatment timeline, so you can compare imaging across initial diagnosis, standard of care, first remission, “founder mode cancer care”, and second remission. It’s viewable in OHIF, the standard open-source viewer used in radiology research. Given the long history of applying deep learning to radiology, we’re hoping to see a variety of methods and models applied to my data. If you find something interesting, please reach out to [email protected], or if you know a researcher who should see this, tag them below. Thanks to Terra LaFerriere for organizing these images, and Kamil Slowikowski for building osteosarc.com into the resource it is.

Is AI Flooding Medical Journals With 'Meaningless' Research? medpagetoday.com/special-rep… "If medical schools and universities don't want the literature to be overwhelmed with garbage," ... "then they should probably stop giving every applicant ... an incentive to publish a lot of crap."

I originally made an app for myself to track a new medication I was trying, added more features, and a friend pointed out that she might use it, too. This helped me realize I would probably use it a few different ways for occasional med tracking, too. (1/3)

I kept hitting this problem across projects: stuck mapping genes before I could do the biology I actually cared about. 🤦💻 I coded my own solutions — then realized everyone else was doing the same. So much wasted time! So I decided to tidy up my code and release it. 🚀