Archive for the ‘rants’ Category

The curse of the big table

Wednesday, June 3rd, 2015

As an Area Editor for Pattern Recognition Letters, I’m frequently confronted with papers containing big tables of results. It is often the deblurring and denoising papers that (obviously using PSNR as a quality metric!) display lots of large tables comparing the proposed method with the state of the art on a set of images. I’m seriously tired of this. Now I’ve set my foot down, and asked an author to remove the table and provide a plot instead. In this post I will show what is wrong with the tables and propose a good alternative.


Computer vision is hard!

Wednesday, September 24th, 2014

Today’s xkcd comic is relevant to this blog.

xkcd comic #1425

Mouse-over text: “In the 60s, Marvin Minsky assigned a couple of undergrads to spend the summer programming a computer to use a camera to identify objects in a scene. He figured they’d have the problem solved by the end of the summer. Half a century later, we’re still working on it.”

Proper counting

Tuesday, September 23rd, 2014

I just came across an editorial in the Journal of the American Society of Nephrology (Kirsten M. Madsen, Am Soc Nephrol 10(5):1124-1125, 1999), which states:

A considerable number of manuscripts submitted to the Journal include quantitative morphologic data based on counts and measurements of profiles observed in tissue sections or projected images. Quite often these so-called morphometric analyses are based on assumptions and approximations that cannot be verified and therefore may be incorrect. Moreover, many manuscripts have insufficient descriptions of the sampling procedures and statistical analyses in the Methods section, or it is apparent that inappropriate (biased) sampling techniques were used. Because of the availability today of many new and some old stereologic methods and tools that are not based on undeterminable assumptions about size, shape, or orientation of structures, the Editors of the Journal believe that it is time to dispense with the old, often biased, model-based stereology and change the way we count and measure.

It then goes on to say that the journal would require appropriate stereological methods be employed for quantitative morphologic studies. I have never read a paper in this journal, but certainly hope that they managed to hold on to this standard during the 15 years since this editorial was written. Plenty of journals have not come this far yet.

Automated image-based diagnosis

Saturday, August 24th, 2013

Nowhere is it as difficult to get a fully automatic image analysis system accepted and used in practice as in the clinic. Not only are physicians sceptical of technology that makes them irrelevant, but an automated system has to produce a perfect result, a correct diagnosis for 100% of the cases, to be trusted without supervision. And of course this is impossible to achieve. In fact, even if the system has a better record than an average (or a good) physician, it is unlikely that it is the same cases where the system and the physician are wrong. Therefore, the combination of machine + physician is better than the machine, and thus the machine should not be used without the physician.

What often happens then is that the system is tuned to yield a near 100% sensitivity (to miss only very few positives), and thus has a very low specificity (that is, it marks a lot of negative tests as positive). The system is heavily biased to the positives. The samples marked by the system as negative are almost surely negative, whereas the samples marked as positive (or, rather, suspect) are reviewed by the physician. This is supposed to lighten the workload of the physician. This seems nice and useful, no? What is the problem?


How did this get published?

Wednesday, December 19th, 2012

“How did this get published?” is a question I regularly ask myself when reading new papers coming out. I just came across another one of these jewels, and because the topic is that of a previous blog post here, I thought I’d share my frustration with you.