We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App

Machine Learning Solution Assists Pathologists in Detection of Precancerous Cervical Lesions

By LabMedica International staff writers
Posted on 15 Jun 2023
Print article
Image: A machine learning solution assists pathologists in detection of cervical dysplasia (Photo courtesy of Freepik)
Image: A machine learning solution assists pathologists in detection of cervical dysplasia (Photo courtesy of Freepik)

Cervical cancer ranks as the fourth most prevalent cancer in women, with a reported 604,000 new occurrences in 2020, as per the World Health Organization (WHO). Yet, it stands out as one of the most successfully preventable and treatable cancers, provided that it is detected early and managed appropriately. Therefore, early detection of pre-cancerous lesions is critical for disease prevention. Now, researchers have developed an innovative method using large, high-resolution images to detect crucial pre-cancerous lesions.

A team of researchers from INESC TEC (Porto, Portugal) and IMP Diagnostics (Porto, Portugal) has designed a machine learning solution to aid pathologists in detecting cervical dysplasia, making the diagnosis process of new samples fully automatic. This is among the first published works to utilize complete slides. The researchers set out to develop machine learning models to support the subjective classification of lesions in the squamous epithelium - the protective tissue layer against microorganisms - using whole-slide images (WSI) that contain information from the entire tissue.

The team developed a weakly-supervised methodology - a machine learning method that combines annotated and non-annotated data in the model training phase to classify cervical dysplasia. This technique proves particularly beneficial considering the difficulty of obtaining pathology data annotations: the large image sizes make the annotation process extremely time-consuming, tedious, and highly subjective. This methodology enables researchers to establish models with high performance, even when there's some missing information during the training phase. The resulting model can then grade cervical dysplasia, or abnormal cell growth on the surface, as low (LSIL) or high-grade intraepithelial squamous lesions (HSIL). Given the complexity and subjective nature of the classification process, these machine learning models can provide valuable assistance to pathologists. Furthermore, these systems could act as an early warning mechanism for suspicious cases, alerting pathologists to instances that warrant closer examination.

"In the detection of cervical dysplasia, this was one of the first published works that use the full slides, following an approach that includes the segmentation and subsequent classification of the areas of interest, making the diagnosis of new samples completely automatic," explained Sara Oliveira, a researcher at INESC TEC.

Related Links:
IMP Diagnostics 

Platinum Member
COVID-19 Rapid Test
OSOM COVID-19 Antigen Rapid Test
Magnetic Bead Separation Modules
Anti-Cyclic Citrullinated Peptide Test
GPP-100 Anti-CCP Kit
Gold Member
ADAMTS-13 Protease Activity Test
ATS-13 Activity Assay

Print article


Clinical Chemistry

view channel
Image: The 3D printed miniature ionizer is a key component of a mass spectrometer (Photo courtesy of MIT)

3D Printed Point-Of-Care Mass Spectrometer Outperforms State-Of-The-Art Models

Mass spectrometry is a precise technique for identifying the chemical components of a sample and has significant potential for monitoring chronic illness health states, such as measuring hormone levels... Read more


view channel
Image: The CAPILLARYS 3 DBS devices have received U.S. FDA 510(k) clearance (Photo courtesy of Sebia)

Next Generation Instrument Screens for Hemoglobin Disorders in Newborns

Hemoglobinopathies, the most widespread inherited conditions globally, affect about 7% of the population as carriers, with 2.7% of newborns being born with these conditions. The spectrum of clinical manifestations... Read more


view channel
Image: A false color scanning election micrograph of lung cancer cells grown in culture (Photo courtesy of Anne Weston)

AI Tool Precisely Matches Cancer Drugs to Patients Using Information from Each Tumor Cell

Current strategies for matching cancer patients with specific treatments often depend on bulk sequencing of tumor DNA and RNA, which provides an average profile from all cells within a tumor sample.... Read more


view channel
Image: Microscope image showing human colorectal cancer tumor with Fusobacterium nucleatum stained in a red-purple color (Photo courtesy of Fred Hutch Cancer Center)

Mouth Bacteria Test Could Predict Colon Cancer Progression

Colon cancer, a relatively common but challenging disease to diagnose, requires confirmation through a colonoscopy or surgery. Recently, there has been a worrying increase in colon cancer rates among younger... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.