Uolo - Automatic object detection and segmentation in biomedical images

Authors: Araújo, T., Aresta, G., Galdran, A., Costa, P., Mendonça, A.M. and Campilho, A.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 11045 LNCS

Pages: 165-173

eISSN: 1611-3349

ISSN: 0302-9743

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

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.

Source: Scopus

UOLO - Automatic Object Detection and Segmentation in Biomedical Images

Authors: Araujo, T., Aresta, G., Galdran, A., Costa, P., Mendonca, A.M. and Campilho, A.

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

Volume: 11045

Pages: 165-173

eISSN: 1611-3349

ISSN: 0302-9743

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

Source: Web of Science (Lite)

UOLO - Automatic Object Detection and Segmentation in Biomedical Images.

Authors: Araújo, T., Aresta, G., Galdran, A., Costa, P., Mendonça, A.M. and Campilho, A.

Editors: Stoyanov, D. et al.

Journal: DLMIA/ML-CDS@MICCAI

Volume: 11045

Pages: 165-173

Publisher: Springer

https://doi.org/10.1007/978-3-030-00889-5

Source: DBLP

UOLO - automatic object detection and segmentation in biomedical images

Authors: Araújo, T., Aresta, G., Galdran, A., Costa, P., Mendonça, A.M. and Campilho, A.

Journal: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings. 165-173

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

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.

http://dx.doi.org/10.1007/978-3-030-00889-5_19

Source: arXiv