Improving and evaluating deep learning models of cellular organization.

ABSTRACT:

Motivation

Cells contain dozens of major organelles and thousands of other structures, many of which vary extensively in their number, size, shape and spatial distribution. This complexity and variation dramatically complicates the use of both traditional and deep learning methods to build accurate models of cell organization. Most cellular organelles are distinct objects with defined boundaries that do not overlap, while the pixel resolution of most imaging methods is n sufficient to resolve these boundaries. Thus while cell organization is conceptually object-based, most current methods are pixel-based. Using extensive image collections in which particular organelles were fluorescently labeled, deep learning methods can be used to build conditional autoencoder models for particul

SUBMITTER: Sun H 

PROVIDER: S-EPMC9710556 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature