TY - GEN
T1 - Deep Learning on SDF for Classifying Brain Biomarkers
AU - Yang, Zhangsihao
AU - Wu, Jianfeng
AU - Thompson, Paul M.
AU - Wang, Yalin
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Biomarkers are one of the primary medical signs to facilitate the early detection of Alzheimer's disease. The small beta-amyloid (Aβ) peptide is an important indicator for the disease. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Thus a less invasive and cheaper approach is demanded. MRI which has been used widely in preclinical AD has recently shown the capability to predict brain Aβ positivity. This motivates us to develop a method, SDF sparse convolution, taking MRI to predict Aβ positivity. We obtain subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and use our method to discriminate Aβ positivity. Theoretically, we provide analysis towards the understanding of what the network has learned. Empirically, it shows strong performance on par or even better than state of the art.
AB - Biomarkers are one of the primary medical signs to facilitate the early detection of Alzheimer's disease. The small beta-amyloid (Aβ) peptide is an important indicator for the disease. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Thus a less invasive and cheaper approach is demanded. MRI which has been used widely in preclinical AD has recently shown the capability to predict brain Aβ positivity. This motivates us to develop a method, SDF sparse convolution, taking MRI to predict Aβ positivity. We obtain subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and use our method to discriminate Aβ positivity. Theoretically, we provide analysis towards the understanding of what the network has learned. Empirically, it shows strong performance on par or even better than state of the art.
UR - http://www.scopus.com/inward/record.url?scp=85122543345&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85122543345&partnerID=8YFLogxK
U2 - 10.1109/EMBC46164.2021.9630850
DO - 10.1109/EMBC46164.2021.9630850
M3 - Conference contribution
C2 - 34891469
AN - SCOPUS:85122543345
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1051
EP - 1054
BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Y2 - 1 November 2021 through 5 November 2021
ER -