Identifying Cell Types in Lung Cancer Pathology Images Using Deep Learning
Principal Investigator
Name
guiyun Chen
Degrees
M.S.E
Institution
Jiangnan University
Position Title
Master student
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-1189
Initial CDAS Request Approval
Jan 17, 2024
Title
Identifying Cell Types in Lung Cancer Pathology Images Using Deep Learning
Summary
Lung cancer remains one of the leading causes of mortality worldwide, with its prognosis heavily dependent on early detection and accurate diagnosis. Traditional methods of cell type identification are time-consuming and subject to inter-observer variability. Deep learning, a subset of artificial intelligence, has shown promise in image recognition tasks and has the potential to revolutionize the field of digital pathology by providing a high-throughput, reproducible, and accurate means of cell type classification.
Aims
1.To develop a deep learning model capable of accurately identifying different cell types within lung cancer pathological images.
2.To compare the performance of this model with traditional histopathological analysis methods in terms of accuracy, speed, and reproducibility.
3.To validate the model across diverse datasets, including those from different institutions and patient demographics, to ensure generalizability.
Collaborators
xiangPan, Jiangnan University