用迁移学习优化医学图像分类的CNN架构，助力选型决策
Development of CNN Architectures using Transfer Learning Methods for Medical Image Classification
- 基于时间线映射，分析迁移学习在CNN中的应用路径
- 揭示主流CNN架构在医学图像分类中的性能表现与演进规律
- 为临床场景提供可复现的模型选型依据，适合医疗AI研发者
近年来，基于深度学习的架构应用显著增长。例如，深度学习在医学图像分类中取得了突破性成果。卷积神经网络（CNN）被广泛应用于医学图像分类与分割。与此同时，迁移学习已成为提升深度学习模型效率和准确率的重要工具。本文通过时间线映射模型，研究了迁移学习技术在医学图像分类领域中CNN架构的发展历程，系统梳理了关键挑战与应对策略。研究结果有助于在实际应用中选择最优且前沿的CNN架构，支持高效、可靠的模型决策。
原文摘要 · Abstract (English)
The application of deep learning-based architecture has seen a tremendous rise in recent years. For example, medical image classification using deep learning achieved breakthrough results. Convolutional Neural Networks (CNNs) are implemented predominantly in medical image classification and segmentation. On the other hand, transfer learning has emerged as a prominent supporting tool for enhancing the efficiency and accuracy of deep learning models. This paper investigates the development of CNN architectures using transfer learning techniques in the field of medical image classification using a timeline mapping model for key image classification challenges. Our findings help make an informed decision while selecting the optimum and state-of-the-art CNN architectures.
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