计算机科学
人工智能
分割
计算机视觉
图像分割
目标检测
像素
跳跃式监视
尺度空间分割
模式识别(心理学)
基于分割的对象分类
对象(语法)
集合(抽象数据类型)
程序设计语言
作者
Teresa Araújo,Guilherme Aresta,Adrián Galdrán,Pedro Costa,Ana Maria Mendonça,Aurélio Campilho
标识
DOI:10.1007/978-3-030-00889-5_19
摘要
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.
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