195 citations · 293 across the 3 of their papers we have counts for
3 papers
cs.CL2018★ 3 cited
Efficient and Accurate Abnormality Mining from Radiology Reports with Customized False Positive Reduction
Nithya Attaluri, Ahmed Nasir, Carolynne Powe +7
Obtaining datasets labeled to facilitate model development is a challenge for most machine learning tasks. The difficulty is heightened for medical imaging, where data itself is li…
cs.CV2018★ 95 cited
Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions
Li Yao, Jordan Prosky, Eric Poblenz +2
Diagnostic imaging often requires the simultaneous identification of a multitude of findings of varied size and appearance. Beyond global indication of said findings, the predictio…
cs.CV2017★ 195 cited
Learning to diagnose from scratch by exploiting dependencies among labels
Li Yao, Eric Poblenz, Dmitry Dagunts +3
The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translat…