NDLI: Landmark-based deep multi-instance learning for brain disease diagnosis

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Landmark-based deep multi-instance learning for brain disease diagnosis

Content Provider	Scilit
Author	Liu, Mingxia Zhang, Jun Adeli, Ehsan Shen, Dinggang
Copyright Year	2017
Description	Journal: Medical Image Analysis 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 ROIs 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.
Related Links	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6203325/pdf
Ending Page	168
Page Count	12
Starting Page	157
ISSN	13618415
e-ISSN	13618423
DOI	10.1016/j.media.2017.10.005
Journal	Medical Image Analysis
Volume Number	43
Language	English
Publisher	Elsevier BV
Publisher Date	2017-10-27
Access Restriction	Open
Subject Keyword	Journal: Medical Image Analysis Medical Informatics Convolutional Neural Network Multi-instance Learning Brain Disease
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Radiology, Nuclear Medicine and Imaging Health Informatics Computer Vision and Pattern Recognition Radiological and Ultrasound Technology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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