# Fully Automatic Data Labeling for Ultrasound Screen Detection

arXiv: https://arxiv.org/abs/2511.13197 · HTML: https://mlsanity.com/paper/2511.13197/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Auxiliary Tasks in Multi-task Learning](https://mlsanity.com/paper/1805.06334/) (2018)
- [Numerical Coordinate Regression with Convolutional Neural Networks](https://mlsanity.com/paper/1801.07372/) (2018)
- [Contrastive Learning for View Classification of Echocardiograms](https://mlsanity.com/paper/2108.03124/) (2021)
- [Multi-task UNet: Jointly Boosting Saliency Prediction and Disease Classification on Chest X-ray Images](https://mlsanity.com/paper/2202.07118/) (2022)
- [Screen Tracking for Clinical Translation of Live Ultrasound Image Analysis Methods](https://mlsanity.com/paper/2007.06272/) (2020)

## Abstract (arXiv)

Ultrasound (US) machines display images on a built-in monitor, but routine transfer to hospital systems relies on DICOM. We propose a fully automatic method to generate labeled data that can be used to train a screen detector model, and a pipeline to use that model to extract and rectify the US image from a photograph of the monitor, without any need for human annotation. This removes the DICOM bottleneck and enables rapid testing and prototyping of new algorithms. In a proof-of-concept study, the rectified images retained enough visual fidelity to classify cardiac views with a balanced accuracy of 0.79 with respect to the native DICOMs., the rectified images retained enough visual fidelity to classify cardiac views with a balanced accuracy of 0.79 with respect to the native DICOMs.