Journal:Histopathology image classification: Highlighting the gap between manual analysis and AI automation
| Full article title | Histopathology image classification: Highlighting the gap between manual analysis and AI automation |
|---|---|
| Journal | Frontiers in Oncology |
| Author(s) | Doğan, Refika S.; Yılmaz, Bülent |
| Author affiliation(s) | Abdullah Gül University, Gulf University for Science and Technology |
| Primary contact | refikasultan dot dogan at agu dot edu dot tr |
| Editors | Pagador, J. Blas |
| Year published | 2024 |
| Volume and issue | 13 |
| Article # | 1325271 |
| DOI | 10.3389/fonc.2023.1325271 |
| ISSN | 2234-943X |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1325271/full |
| Download | https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1325271/pdf (PDF) |
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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 (AI) algorithms, such as convolutional neural networks (CNNs), 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 7,180 non-overlapping images from 50 patients for testing. The study compares two distinct approaches, training AI-based algorithms and manual machine learning (ML) 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 AI-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 AI-based methods are generalizable, allowing them to be integrated into histopathology diagnostics procedures and improve diagnostic accuracy and efficiency. The CNN model outperforms existing ML 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 AI to assess histopathological images.
Keywords: data science, image processing, artificial intelligence, histopathology images, colon cancer
Introduction
Histopathological image analysis is a fundamental method for diagnosing and screening cancer, especially in disorders affecting the digestive system. It is a type of analysis used to diagnose and treat cancer. In the case of pathologists, the physical and visual examinations of complex images often come in the form of resolutions up to 100,000 x 100,000 pixels. On the other hand, the method of pathological image analysis has long been dependent on this approach, known for its time-consuming and labor-intensive characteristics. New approaches are needed to increase the efficiency and accuracy of pathological image analysis. Up to this point, the realization of digital pathology approaches has seen significant progress. Digitization of high-resolution histopathology images allows comprehensive analysis using complex computational methods. As a result, there has been a significant increase in interest in medical image analysis for creating automatic models that can precisely categorize relevant tissues and structures in various clinical images. Early research in this area focused on predicting the malignancy of colon lesions and distinguishing between malignant and normal tissue by extracting features from microscopic images. Esgiar et al. [1] analyzed 44 healthy and 58 cancerous features obtained from microscope images. As a result of the analysis, the percentage of occurrence matrices used equaled 90 percent. These first steps form the basis for more complex procedures that integrate rapid image processing techniques and the functions of visualization software. Digital pathology has recently emerged as a widespread diagnostic tool, primarily through artificial intelligence (AI) algorithms. [2, 3] It has demonstrated impressive capability in processing pathology images in an advanced manner. [4, 5] Advanced techniques, identification of tumors, detection of metastasis, and assessment of patient prognosis are utilized regularly. Through the utilization of this process, the automatic segmentation of pathological images, generation of predictions, and the utilization of relevant observations from this complex visual data have been planned. [6, 7]
Convolutional neural networks (CNNs) have received significant focus among various machine learning (ML) techniques in AI research. As a result of the application of deep learning in previous biological research, ML has been extensively accepted and used. [8–10] CNNs distinguish themselves from other ML methods because of their extraordinary accuracy, generalization capacity, and computational economy. Each patient’s histopathology photographs contain important quantitative data, known as hematoxylin-eosin (H&E) stained tissue slides. Notably, Kather et al. [11] have explored the potential of CNN-based approaches to predict disease progression directly from the available H&E images. In a retrospective study, their findings underscored CNN’s remarkable ability to assess the human tumor microenvironment and prognosticate outcomes based on the analysis of histopathological images. This breakthrough showcases the transformative potential of such AI-based methodologies in revolutionizing the field of medical image analysis, offering new avenues for efficient and objective diagnostic and prognostic assessments.
References
Notes
This presentation is faithful to the original, with only a few minor changes to presentation, though grammar and word usage was substantially updated for improved readability. In some cases important information was missing from the references, and that information was added.
