Pap Smear Cell Classification Challenge (PS3C)

Cervical cancer is the fourth most common cancer among women globally, with over 600,000 new cases and more than 300,000 deaths annually. Early detection through Pap smear screening plays a vital role in reducing mortality by identifying precancerous lesions. However, traditional methods of analyzing Pap smear samples are resource-intensive, time-consuming, and highly dependent on the expertise of cytologists. These challenges highlight the need for automation in cervical cancer screening, particularly in resource-limited settings.

The Pap Smear Cell Classification Challenge (PS3C), part of the 22. IEEE ISBI 2025 Challenge Program, invites participants to address this critical problem in medical imaging: the automated classification of cervical cell images extracted from Pap smears. Using cutting-edge machine learning techniques, participants will develop models to classify test images into one of three categories:

• Healthy: Normal cells without observable abnormalities.
• Unhealthy: Abnormal cells that indicate potential pathological changes.
• Rubbish: Images unsuitable for evaluation due to artifacts or poor quality.

Further details can be found at the webpage of the conference.

Organizers:

• Dávid Kupás, Department of Data Science and Visualization, University of Debrecen
• Balázs Harangi, Department of Data Science and Visualization, University of Debrecen
• Nicolai Spicher, Department of Medical Informatics, Universitätsmedizin Göttingen
• Péter Kovács, Department of Numerical Analysis, Eötvös Loránd University
• András Hajdu, Department of Data Science and Visualization, University of Debrecen
• Ilona Kovács, Department of Pathology, Kenezy Gyula University Hospital and Clinic