TY - GEN
T1 - AI-based Method for Thyroid Cancer in Histopathology Imaging
T2 - 2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024
AU - Shabrina, Nabila Husna
AU - Gunawan, Dadang
AU - Harahap, Agnes Stephanie
AU - Ham, Maria Francisca
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The global incidence of thyroid cancer has increased over the past three decades, making it the seventh most common cancer worldwide. Conventional histopathology, the gold standard for diagnosing cancer, has limitations such as time-consuming manual processes and inconsistencies between experts. Digital and computer-aided Pathology have emerged to address these limitations, and recent advancements in Artificial Intelligence have facilitated their use. This paper presents a systematic literature review investigating the current state of AI-based methods for diagnosing thyroid cancer using histopathological images. Three critical questions guided the review, focusing on image processing and classification methods currently used for identifying thyroid cancer in histopathological images, as well as the dataset utilized for conducting this research. This systematic review revealed essential information on the current trends and challenges of AI-based methods for thyroid cancer histopathology images.
AB - The global incidence of thyroid cancer has increased over the past three decades, making it the seventh most common cancer worldwide. Conventional histopathology, the gold standard for diagnosing cancer, has limitations such as time-consuming manual processes and inconsistencies between experts. Digital and computer-aided Pathology have emerged to address these limitations, and recent advancements in Artificial Intelligence have facilitated their use. This paper presents a systematic literature review investigating the current state of AI-based methods for diagnosing thyroid cancer using histopathological images. Three critical questions guided the review, focusing on image processing and classification methods currently used for identifying thyroid cancer in histopathological images, as well as the dataset utilized for conducting this research. This systematic review revealed essential information on the current trends and challenges of AI-based methods for thyroid cancer histopathology images.
KW - AI-based method
KW - histopathological image
KW - systematic literature review
KW - thyroid cancer
UR - http://www.scopus.com/inward/record.url?scp=85219643467&partnerID=8YFLogxK
U2 - 10.1109/HEALTHCOM60970.2024.10880736
DO - 10.1109/HEALTHCOM60970.2024.10880736
M3 - Conference contribution
AN - SCOPUS:85219643467
T3 - 2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024
BT - 2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 November 2024 through 20 November 2024
ER -
