A systematic review of deep learning-based cervical cytology screening: from cell identification to whole slide image analysis

Abstract

Cervical cancer is one of the most common cancers in daily life. Early detection and diagnosis can effectively help facilitate subsequent clinical treatment and management. With the growing advancement of artificial intelligence (AI) and deep learning (DL) techniques, an increasing number of computer-aided diagnosis (CAD) methods based on deep learning have been applied in cervical cytology screening. In this paper, we survey more than 80 publications since 2016 to provide a systematic and comprehensive review of DL-based cervical cytology screening. First, we provide a concise summary of the medical and biological knowledge pertaining to cervical cytology, since we hold a firm belief that a comprehensive biomedical understanding can significantly contribute to the development of CAD systems. Then, we collect a wide range of public cervical cytology datasets. Besides, image analysis approaches and applications including cervical cell identification, abnormal cell or area detection, cell region segmentation and cervical whole slide image diagnosis are summarized. Finally, we discuss the present obstacles and promising directions for future research in automated cervical cytology screening.

Decoding sarcasm: unveiling nuances in newspaper headlines

“…The use of "innovative" and the contrast between "easing commutes" and "adding to urban gridlock" introduces skepticism, prompting readers to question the effectiveness of the proposed traffic solutions. Deciphering these subtleties poses a challenge for both automated systems and human interpreters within the concise format of newspaper titles [9], [10]. Headlines, consumed in quick glances, wield significant influence in shaping perceptions.…”