UOLO - Automatic Object Detection and Segmentation in Biomedical Images

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Teresa Araújo (Author) Other; Guilherme Aresta (Author) Other; Adrian Galdran (Author) Other; Pedro Costa (Author) Other; Ana Maria Mendonça (Author) FEUP; Aurélio Campilho (Author) FEUP

Conference proceedings International

Title: DEEP LEARNING IN MEDICAL IMAGE ANALYSIS AND MULTIMODAL LEARNING FOR CLINICAL DECISION SUPPORT, DLMIA 2018

Pages: 165-173

4th International Workshop on Deep Learning in Medical Image Analysis (DLMIA) / 8th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS)

Granada, SPAIN, SEP 20, 2018

Indexing

ISI Web of Knowledge - 9 Citations

Scopus - 12 Citations

Other information

Authenticus ID: P-00P-NA6

DOI: 10.1007/978-3-030-00889-5_19

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9