RT Journal Article
T1 Label Aided Deep Ranking for the Automatic Diagnosis of Parkinsonian Syndromes.
A1 Ortiz-García, Andrés
A1 Martínez-Murcia, Francisco Jesús
A1 Munilla-Fajardo, Jorge
A1 Górriz-Sáez, Juan Manuel
A1 Ramírez, Javier
K1 Parkinson, Enfermedad de - Diagnóstico - Proceso de datos
K1 Diagnóstico - Proceso de datos
K1 Medicina - Proceso de datos
K1 Inteligencia artificial - Aplicaciones médicas
AB Parkinsonism is the second most common neurodegenerative disease in the world. Its diagnosis usually relies on visual analysis of Emission Computed Tomography (SPECT) images acquired using 123I − io f lupane radiotracer. This aims to detect a deficit of dopamine transporters at the striatum. The use of Computer Aided tools for diagnosis based on statistical data processing and machine learning methods have significantly improved the diagnosis accuracy. In this paper we propose a classification method based on Deep Ranking which learns an embedding function that projects the source images into a new space in which samples belonging to the same class are closer to each other, while samples from different classes are moved apart. Moreover, the proposed approach introduces a new cost-sensitive loss function to avoid overfitting due to class imbalance (an usual issue in practical biomedical applications), along with label information to produce sparser embedding spaces. The experiments carried out in this work demonstrate the superiority of the proposed method, improving the diagnosis accuracy achieved by previous methodologies and validate our approach as an efficient way to construct linear classifiers.
PB Elsevier
YR 2018
FD 2018-10-16
LK https://hdl.handle.net/10630/28101
UL https://hdl.handle.net/10630/28101
LA eng
NO Ortiz, Andrés & Martínez-Murcia, Francisco & Munilla, Jorge & Gorriz, Juan & Ramírez, Javier. (2018). Label Aided Deep Ranking for the Automatic Diagnosis of Parkinsonian Syndromes. Neurocomputing. 330. 10.1016/j.neucom.2018.10.074
NO This work was partly supported by the MINECO/FEDER under TEC2015-64718- R and PSI2015-65848-R projects. We gratefully acknowledge the support of NVIDIA Corporation with the donation of one of the GPUs used for this research. PPMI - a pub435 lic - private partnership - is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026