Machine Learning Techniques for Automated Nuclear Atypia Detection in Histopathology Images: A Review

Title

Machine Learning Techniques for Automated Nuclear Atypia Detection in Histopathology Images: A Review

Creator

Varghese J.; Saleema J.S.

Description

Nuclear atypia identification is an important stage in pathology procedures for breast cancer diagnosis and prognosis. The introduction of image processing techniques to automate nuclear atypia identification has made the very tedious, error-prone, and time-consuming procedure of manually observing stained histopathological slides much easier. In the last decade, several solutions for resolving this problem have emerged in the literature, and they have shown positive incremental advancements in this fieldof study. The nuclear atypia count is an important measure to consider when assessing breast cancer. This work provides a comprehensive review of automated nuclear atypia scoring process which includes the current advancements and future prospects for this critical undertaking, which will aid humanity in the fight against cancer. In this study, we examine the various techniques applied in detecting nuclear atypiain breast cancer as well as the major hurdles that must be overcome and the use of benchmark datasets in this domain. This work provides a comprehensive review of automated nuclear atypia scoring process which includes the current advancements and prospects for this critical undertaking, which will aid humanity in the fight against cancer. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-563, pp. 717-740.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Breast cancer; Deep learning; Histopathological image; Machine learning; Nuclear atypia detection; Nuclear pleomorphism

Coverage

Varghese J., Department of Computer Science, Christ Deemed to Be University, Karnataka, Bengaluru, 560029, India, Christ Academy Institute for Advanced Studies, Karnataka, Bengaluru, 560083, India; Saleema J.S., Department of Computer Science, Christ Deemed to Be University, Karnataka, Bengaluru, 560029, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981197401-4

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-19-7402-1_51

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