Cervical whole-slide images dataset for multiclass classification.

ABSTRACT:

Background

The clinical pathway for the prevention and treatment of cervical cancer depends on cytology and then the assessment of biopsy specimens, fragments of tissue removed for histological examination. This can be a significant workload and is an obvious exemplar to explore triage based on machine learning analysis of slides. Limited access to large annotated datasets of human diseased tissue is a major obstacle to developing standards and algorithms that can assist diagnosis.

Results

We present a dataset comprising 2,539 whole-slide images of cervical biopsy specimens, each annotated by several pathologists and consensus on diagnosis and individual features agreed. Each whole-slide image represents 1 slide per patient in iSyntax format, with manual annotations by patho

SUBMITTER: Mohammadi M 

PROVIDER: S-EPMC12751090 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature