# UOLO - automatic object detection and segmentation in biomedical images

Source: [UOLO - automatic object detection and segmentation in biomedical images](<https://www.sota2.com/research/paper/68fa65de1e209131cea0cf5f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T05:58:47.572Z

Authors: Teresa Ara\\'ujo, Guilherme Aresta, Adrian Galdran, Pedro Costa, Ana Maria Mendon\\c{c}a, Aur\\'elio Campilho

Published: 2018-10-09T13:53:13.000Z

## Abstract

We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images. UOLO consists of an object segmentation module which intermediate abstract representations are processed and used as input for object detection. The resulting system is optimized simultaneously for detecting a class of objects and segmenting an optionally different class of structures. UOLO is trained on a set of bounding boxes enclosing the objects to detect, as well as pixel-wise segmentation information, when available. A new loss function is devised, taking into account whether a reference segmentation is accessible for each training image, in order to suitably backpropagate the error. We validate UOLO on the task of simultaneous optic disc (OD) detection, fovea detection, and OD segmentation from retinal images, achieving state-of-the-art performance on public datasets.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1810.05729>)

[PDF](<https://arxiv.org/pdf/1810.05729.pdf>)