AIMC Topic: Papanicolaou Test

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DeepPap: Deep Convolutional Networks for Cervical Cell Classification.

IEEE journal of biomedical and health informatics
Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. However, the success of most trad...

Impact of the Genius Digital Diagnostics System on workflow and accuracy compared with the ThinPrep Imaging System for review of ThinPrep Papanicolaou tests.

American journal of clinical pathology
OBJECTIVE: In this study, we compared the workflow of the Genius Digital Diagnostics System (Hologic, Inc) with our current workflow based on the ThinPrep Imaging System (Hologic, Inc) to assess potential efficiencies associated with digitalization o...

Discriminant study of cervical squamous cells based on computer image analysis.

Medicine
This study aimed to explore a discriminant method for cervical squamous epithelial cells based on computer image analysis to establish a foundation for artificial intelligence diagnosis of cervical cancer. A total of 1682 cells were captured from 53 ...

CervicalMethDx: A Precision DNA Methylation Test to Identify Risk of High-Grade Intraepithelial Lesions in Cervical Cancer Screening Algorithms.

Cancer prevention research (Philadelphia, Pa.)
UNLABELLED: Cervical cancer is one of the most common cancers in women. Despite progress in prevention and success in early detection through cytologic screening and human papillomavirus (HPV) detection, there remains a challenge in triaging women ap...

Assessment of the efficacy and accuracy of cervical cytology screening with the Hologic Genius Digital Diagnostics System.

Cancer cytopathology
BACKGROUND: Medical technologies powered by artificial intelligence are quickly transforming into practical solutions by rapidly leveraging massive amounts of data processed via deep learning algorithms. There is a necessity to validate these innovat...

Solving the problem of imbalanced dataset with synthetic image generation for cell classification using deep learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The low number of annotated training images and class imbalance in the field of machine learning is a common problem that is faced in many applications. With this paper, we focus on a clinical dataset where cells were extracted in a previous research...

A Partial Label-Based Machine Learning Approach For Cervical Whole-Slide Image Classification: The Winning TissueNet Solution.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Cervical cancer is the fourth most common cancer in women worldwide. To determine early treatment for patients, it is critical to accurately classify the cervical intraepithelial lesion status based on a microscopic biopsy. Lesion classification is a...