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Histopathology image classification: highlighting the gap between manual analysis and AI automation.

作者信息

Doğan Refika Sultan, Yılmaz Bülent

机构信息

Department of Bioengineering, Abdullah Gül University, Kayseri, Türkiye.

Biomedical Instrumentation and Signal Analysis Laboratory, Abdullah Gül University, Kayseri, Türkiye.

出版信息

Front Oncol. 2024 Jan 17;13:1325271. doi: 10.3389/fonc.2023.1325271. eCollection 2023.


DOI:10.3389/fonc.2023.1325271
PMID:38298445
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10827850/
Abstract

The field of histopathological image analysis has evolved significantly with the advent of digital pathology, leading to the development of automated models capable of classifying tissues and structures within diverse pathological images. Artificial intelligence algorithms, such as convolutional neural networks, have shown remarkable capabilities in pathology image analysis tasks, including tumor identification, metastasis detection, and patient prognosis assessment. However, traditional manual analysis methods have generally shown low accuracy in diagnosing colorectal cancer using histopathological images. This study investigates the use of AI in image classification and image analytics using histopathological images using the histogram of oriented gradients method. The study develops an AI-based architecture for image classification using histopathological images, aiming to achieve high performance with less complexity through specific parameters and layers. In this study, we investigate the complicated state of histopathological image classification, explicitly focusing on categorizing nine distinct tissue types. Our research used open-source multi-centered image datasets that included records of 100.000 non-overlapping images from 86 patients for training and 7180 non-overlapping images from 50 patients for testing. The study compares two distinct approaches, training artificial intelligence-based algorithms and manual machine learning models, to automate tissue classification. This research comprises two primary classification tasks: binary classification, distinguishing between normal and tumor tissues, and multi-classification, encompassing nine tissue types, including adipose, background, debris, stroma, lymphocytes, mucus, smooth muscle, normal colon mucosa, and tumor. Our findings show that artificial intelligence-based systems can achieve 0.91 and 0.97 accuracy in binary and multi-class classifications. In comparison, the histogram of directed gradient features and the Random Forest classifier achieved accuracy rates of 0.75 and 0.44 in binary and multi-class classifications, respectively. Our artificial intelligence-based methods are generalizable, allowing them to be integrated into histopathology diagnostics procedures and improve diagnostic accuracy and efficiency. The CNN model outperforms existing machine learning techniques, demonstrating its potential to improve the precision and effectiveness of histopathology image analysis. This research emphasizes the importance of maintaining data consistency and applying normalization methods during the data preparation stage for analysis. It particularly highlights the potential of artificial intelligence to assess histopathological images.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39e/10827850/2244018c3bef/fonc-13-1325271-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39e/10827850/51990dfcf282/fonc-13-1325271-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39e/10827850/3559cf674736/fonc-13-1325271-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39e/10827850/97bb9a24724e/fonc-13-1325271-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39e/10827850/909d0c8d811a/fonc-13-1325271-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39e/10827850/fd5eb47846a3/fonc-13-1325271-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39e/10827850/2244018c3bef/fonc-13-1325271-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39e/10827850/51990dfcf282/fonc-13-1325271-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39e/10827850/3559cf674736/fonc-13-1325271-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39e/10827850/97bb9a24724e/fonc-13-1325271-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39e/10827850/909d0c8d811a/fonc-13-1325271-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39e/10827850/fd5eb47846a3/fonc-13-1325271-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39e/10827850/2244018c3bef/fonc-13-1325271-g006.jpg

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Histopathology image classification: highlighting the gap between manual analysis and AI automation.

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引用本文的文献

[1]
Application of AI in the identification of gastrointestinal stromal tumors: a comprehensive analysis based on pathological, radiological, and genetic variation features.

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[2]
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本文引用的文献

[1]
An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images.

Front Oncol. 2023-11-13

[2]
Artificial intelligence in diagnostic pathology.

Diagn Pathol. 2023-10-3

[3]
Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare.

Cureus. 2023-8-10

[4]
Automatic Classification of Histopathology Images across Multiple Cancers Based on Heterogeneous Transfer Learning.

Diagnostics (Basel). 2023-3-28

[5]
Explainability and causability in digital pathology.

J Pathol Clin Res. 2023-7

[6]
Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding.

Brief Bioinform. 2023-1-19

[7]
Application of Artificial Intelligence in Pathology: Trends and Challenges.

Diagnostics (Basel). 2022-11-15

[8]
Robust Random Forest-Based All-Relevant Feature Ranks for Trustworthy AI.

Stud Health Technol Inform. 2022-5-25

[9]
Potential of deep representative learning features to interpret the sequence information in proteomics.

Proteomics. 2022-1

[10]
Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review.

Vet Pathol. 2022-1

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