Deep learning approaches for pathological image classification

IF 2.3 Q1 DENTISTRY, ORAL SURGERY & MEDICINE
Masayuki Tsuneki
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引用次数: 0

Abstract

Background

Computer-aided diagnosis using deep learning has emerged as a transformative tool in pathology that enables the automated and consistent interpretation of whole slide images. Among the key tasks in pathological image analysis, classification-based deep learning models have shown promise in distinguishing cancer subtypes and predicting genetic or molecular features. However, challenges remain due to the limited availability of high-quality labeled datasets, particularly for rare cancers, which hinders the widespread applicability of conventional data-driven approaches.

Highlight

The methodology for developing classification-based deep learning models involves data preparation, annotation strategies, training techniques, and performance evaluation. Core supervised learning approaches are used alongside convolutional and recurrent neural networks to enhance classification performance. In scenarios in which training datasets are scarce, transfer learning serves as an effective strategy to improve the model training efficiency. In addition, this review introduces emerging techniques such as the use of simulators to generate synthetic data and formula-driven approaches that simulate realistic scenarios, with the aim of overcoming the limitations of conventional training datasets. Visualization tools such as probability heatmaps are critical for model interpretability and validation.

Conclusion

Classification-based deep learning models are expected to play an increasing role in precision medicine by expanding capabilities beyond diagnosis to include prognostic prediction and treatment decision making. With innovations in augmented intelligence, deep learning models can comprehensively support clinicians across the diagnostic and therapeutic spectrum, particularly in an era of increasing demand and limited human resources in pathology.
病理图像分类的深度学习方法
使用深度学习的计算机辅助诊断已经成为病理学中的一种变革性工具,可以对整个幻灯片图像进行自动化和一致的解释。在病理图像分析的关键任务中,基于分类的深度学习模型在区分癌症亚型和预测遗传或分子特征方面显示出了希望。然而,由于高质量标记数据集的可用性有限,特别是对于罕见癌症,挑战仍然存在,这阻碍了传统数据驱动方法的广泛适用性。开发基于分类的深度学习模型的方法包括数据准备、注释策略、训练技术和性能评估。核心监督学习方法与卷积和循环神经网络一起使用,以提高分类性能。在训练数据稀缺的情况下,迁移学习是提高模型训练效率的有效策略。此外,本综述还介绍了新兴技术,如使用模拟器生成合成数据和公式驱动的方法来模拟现实场景,目的是克服传统训练数据集的局限性。像概率热图这样的可视化工具对于模型的可解释性和验证是至关重要的。结论基于分类的深度学习模型有望在精准医疗中发挥越来越大的作用,将其功能从诊断扩展到预后预测和治疗决策。随着增强智能的创新,深度学习模型可以在诊断和治疗范围内全面支持临床医生,特别是在病理学需求不断增长而人力资源有限的时代。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Oral Biosciences
Journal of Oral Biosciences DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
4.40
自引率
12.50%
发文量
57
审稿时长
37 days
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