Lightweight and precise cell classification based on holographic tomography-derived refractive index point cloud.

ABSTRACT:

Significance

Accurate cell classification is essential in disease diagnosis and drug screening. Three-dimensional (3D) voxel models derived from holographic tomography effectively capture the internal structural features of cells, enhancing classification accuracy. However, their high dimensionality leads to significant increases in data volume, computational complexity, processing time, and hardware costs, which limit their practical applicability.

Aim

We aim to develop an efficient and accurate cell classification method using 3D refractive index (RI) point cloud data obtained from holographic tomography, focusing on reducing computational complexity without sacrificing classification performance.

Approach

We transformed 3D RI voxel data into point cloud representat

SUBMITTER: Wang H 

PROVIDER: S-EPMC12404102 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature