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Review
. 2024 Feb 8;3(1):100082.
doi: 10.1016/j.iliver.2024.100082. eCollection 2024 Mar.

Artificial intelligence-based pathological analysis of liver cancer: Current advancements and interpretative strategies

Guang-Yu Ding  1   2 , Jie-Yi Shi  1   2 , Xiao-Dong Wang  3 , Bo Yan  4 , Xi-Yang Liu  3 , Qiang Gao  1   2   5   6
Affiliations
Review

Artificial intelligence-based pathological analysis of liver cancer: Current advancements and interpretative strategies

Guang-Yu Ding et al. ILIVER. .

Abstract

In recent years, significant advances have been achieved in liver cancer management with the development of artificial intelligence (AI). AI-based pathological analysis can extract crucial information from whole slide images to assist clinicians in all aspects from diagnosis to prognosis and molecular profiling. However, AI techniques have a "black box" nature, which means that interpretability is of utmost importance because it is key to ensuring the reliability of the methods and building trust among clinicians for actual clinical implementation. In this paper, we provide an overview of current technical advancements in the AI-based pathological analysis of liver cancer, and delve into the strategies used in recent studies to unravel the "black box" of AI's decision-making process.

Keywords: Artificial intelligence; Interpretative model; Liver cancer; Pathology.

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Figures

Fig. 1
Fig. 1
Pipelines of digital pathology using artificial intelligence for the management of liver cancer. AI, artificial intelligence.
Fig. 2
Fig. 2
Comparison of the different workflows of classical machine learning methods and deep learning.
See this image and copyright information in PMC

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