期刊
MEDICAL IMAGE ANALYSIS
卷 43, 期 -, 页码 157-168出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.media.2017.10.005
关键词
Landmark; Convolutional neural network; Multi-instance learning; Brain disease
类别
资金
- NIH [EB006733, EB008374, EB009634, MH100217, AG041721, AG042599, AG010129, AG030514]
- UK Alzheimers Society [RF116]
- GlaxoSmithKline [6GKC]
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [R01EB006733, R01EB009634, R01EB008374] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF MENTAL HEALTH [R01MH100217] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON AGING [R01AG041721, K01AG030514, P30AG010129, R01AG042599, RF1AG053867] Funding Source: NIH RePORTER
In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROls and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches. (C) 2017 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据