The edges of tumors are where growth occurs, and they tell radiologists whether what they see is a tumor or not. You have to keep all these features intact when you compress the image if it is to be useful.

Any technique that improves the performance of radiologists is helpful, but this also means that mammograms can be taken in remote places that are underserved by the medical community.

I want to emphasize that this study does not necessarily imply that compression always improves diagnosis. It means that radiologists can spot and localize features as well or better than before. The technology filters out the noise, if you will. But so far, there is no question that these radiologists did diagnose better using the compressed images.

Any technique that improves the performance of radiologists is helpful, but this also means that mammograms can be taken in remote places that are underserved by the medical community. The mammograms can then be sent electronically to radiologists, who can read the digitized versions knowing they will do at least as well as the original mammograms.

I wanted the algorithm to make all the features important to radiologists degrade at the same rate - both the edges of large tumors and the smallest calcium deposits. I tried several approaches and eventually got a balance that seemed reasonable, based on what radiologists tell me they want.

Any technique that improves the performance of radiologists is helpful, but this also means that mammograms can be taken in remote places that are underserved by the medical community.