Interpretatively automated identification of circulating tumor cells from human peripheral blood with high performance.

ABSTRACT: The detection and analysis of circulating tumor cells (CTCs) would be of aid in a precise cancer diagnosis and an efficient prognosis assessment. However, traditional methods that rely heavily on the isolation of CTCs based on their physical or biological features suffer from intensive labor, thus being unsuitable for rapid detection. Furthermore, currently available intelligent methods are short of interpretability, which creates a lot of uncertainty during diagnosis. Therefore, we propose here an automated method that takes advantage of bright-field microscopic images with high resolution, so as to take an insight into cell patterns. Specifically, the precise identification of CTCs was achieved by using an optimized single-shot multi-box detector (SSD)-based neural network with integrate

SUBMITTER: Li X 

PROVIDER: S-EPMC9947588 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature