Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs.

ABSTRACT:

Background

Histopathology is a gold standard for cancer diagnosis. It involves extracting tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tissue processing procedures result in the introduction of artifacts, which are ultimately transferred to the digitized version of glass slides, known as whole slide images (WSIs). Artifacts are diagnostically irrelevant areas and may result in wrong predictions from deep learning (DL) algorithms. Therefore, detecting and excluding artifacts in the computational pathology (CPATH) system is essential for reliable automated diagnosis.

Methods

In this paper, we propose a mixture of experts (MoE) scheme for detecting five notable artifacts, including damaged tissue, blur, fol

SUBMITTER: Kanwal N 

PROVIDER: S-EPMC11457387 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature