Machine learning-based systems have become instrumental in augmenting global efforts
to combat cervical cancer. A burgeoning area of research focuses on leveraging artificial
intelligence to enhance the cervical screening process, primarily through the exhaustive
examination of Pap smears, traditionally reliant on the meticulous and labor-intensive
analysis conducted by specialized experts. Despite the existence of some comprehensive
and readily accessible datasets, the field is presently constrained by the limited
volume of publicly available images and smears. As a remedy, our work unveils APACC
( A nnotated PA p cell images and smear slices for C ell C lassification), a comprehensive
dataset designed to bridge this gap. The APACC dataset features a remarkable array
of images crucial for advancing research in this field. It comprises 103,675 annotated
cell images, carefully extracted from 107 whole smears, which are further divided
into 21,371 sub-regions for a more refined analysis. This dataset includes a vast
number of cell images from conventional Pap smears and their specific locations on
each smear, offering a valuable resource for in-depth investigation and study.
