Rationale behind most published works in medical imaging trying to reduce false positives In medical image processing, most of the published works try to reduce false positive rate (FPR) while in reality, false negatives are more dangerous than false positives. What is the rationale behind it?
TL;DR: diseases are rare, so the absolute number of false positives is a lot more than that of false negatives.

Let's assume that our system has the same false positive and false negative rate of 1% (pretty good!), and that we're detecting the presence of new cancers this year: 439.2 / 100,000 people, or 0.5% of the population. [source]


No cancer, no detection: 99.5% x 99% = 98.5% (98.505%)
No cancer, detection: 99.5% x 1% = 1.0% (0.995%)
Cancer, detection: 0.5% x 99% = 0.5% (0.495%)
Cancer, no detection: 0.5% x 1% = 0.005%


So we can see that we have a problem: for everyone who has cancer, two people who didn't have cancer wind up with invasive surgery, chemotherapy or radiotherapy. 

For every person who fails to have a present cancer detected, two hundred people receive actively harmful treatment they didn't need and can't really afford.