Research

Medical imaging, and the evidence that says when the model is wrong.

Two recent pieces of work, both on public data, both with the negative results left in. Each card carries one finding; the detail page carries the argument, and the repository stays the source of record.

Endoscopy · acquisition shortcuts

You can tell which hospital a colonoscopy frame came from 96% of the time, without looking at the anatomy, and the standard fix barely helps.

Nine numbers describing the frame the equipment drew (letterboxing, the field-of-view mask, the burned-in date) identify the source dataset out of five, at chance 0.20.

0.961 as acquired
0.859 after the standard crop-and-pad
0.891 from a frozen self-supervised encoder

The same endoscopic frame shown through four preprocessing pipelines, for two source datasets

Four findings, and the one that failed →

Repository

Fetal ultrasound · orientation

Estimating fetal cardiac orientation does not need a trained model: closed-form geometry beats the network by two orders of magnitude.

The useful part is knowing when that shortcut breaks. It does not break where the usual quality score says it should: a mask scoring Dice 0.87 can give a 46° error, one at 0.77 gives 0.22°.

0.28° second-order moments, no training
7.04° trained landmark model, box only
±18° 95% limits of agreement

Six fetal four-chamber ultrasound frames with the annotated and predicted heart axes overlaid, labelled best, median and worst

Why landmarks, and where they break →

Repository

Previously: self-supervised pretraining of a medical-imaging foundation model on a large clinical video corpus under NDA, multi-GPU, with the cross-vendor standardisation and evaluation harness underneath it. Before that, seven years of computer vision and robotics in industry in Germany.

These studies first appeared as a standalone site, still online at portfolio.francescovigni.com. The repositories stay the source of record.

Full background [email protected] GitHub