Siamese Network for Learning Robust Feature of Hippocampi

• 발행기관: 한국스마트미디어학회
• 저자명: Samsuddin Ahmed 정호엽(Ho Yub Jung)
• 간행물 정보: 『스마트미디어저널』Vol9, No.3, 9~17쪽, 전체 9쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2020.09.30

영문 초록

Hippocampus is a complex brain structure embedded deep into the temporal lobe. Studies have shown that this structure gets affected by neurological and psychiatric disorders and it is a significant landmark for diagnosing neurodegenerative diseases. Hippocampus features play very significant roles in region-of-interest based analysis for disease diagnosis and prognosis. In this study, we have attempted to learn the embeddings of this important biomarker. As conventional metric learning methods for feature embedding is known to lacking in capturing semantic similarity among the data under study, we have trained deep Siamese convolutional neural network for learning metric of the hippocampus. We have exploited Gwangju Alzheimer’s and Related Dementia cohort data set in our study. The input to the network was pairs of three-view patches (TVPs) of size 32 × 32× 3. The positive samples were taken from the vicinity of a specified landmark for the hippocampus and negative samples were taken from random locations of the brain excluding hippocampi regions. We have achieved 98.72% accuracy in verifying hippocampus TVPs.

목차

I. INTRODUCTION

II. PRELIMINARY CONCEPTS

III. DATASET

IV. METHODOLOGY

V. EXPERIMENTAL SETUP

VI. RESULT

VII. CONCLUSION

REFERENCES

키워드

Hippocampus Feature Representation Siamese Network