研究目的
To compare traditional versus machine learning–based computer-aided detection (CAD) platforms in breast imaging with a focus on mammography, underscore limitations of traditional CAD, and highlight potential solutions in new CAD systems under development for the future.
研究成果
CAD development for breast imaging is undergoing a paradigm shift with deep learning algorithms offering potential improvements over traditional methods. However, rigorous training, validation, and testing on large datasets are necessary for clinical success. Future CAD systems may enhance radiologist accuracy and provide better clinical outcomes.
研究不足
Traditional CAD has technical limitations including reliance on limited computing resources, small datasets, poor image quality, lack of continuous learning, and human bias in feature design. New deep learning-based CAD faces challenges such as the need for large databases, data privacy issues (e.g., HIPAA compliance), overfitting, 'black box' nature of algorithms, and regulatory hurdles.
1：Experimental Design and Method Selection:
The paper reviews and compares traditional CAD systems based on manual feature design with new deep learning-based CAD systems using convolutional neural networks (CNNs) for mammography. It discusses the theoretical models and algorithms employed, such as CNNs for image classification and detection.
2：Sample Selection and Data Sources:
References various databases used in studies, including Digital Database for Screening Mammography (DDSM), INbreast, Breast Cancer Digital Repository (BCDR), Mammographic Image Analysis Society database (MIAS), and Zebra Mammography Dataset (ZMDS).
3：List of Experimental Equipment and Materials:
Mentions mammography equipment for imaging, but no specific models or brands are detailed in the provided text.
4：Experimental Procedures and Operational Workflow:
Describes the process of training deep neural networks on mammographic images, including data preprocessing, feature extraction, and classification steps. For example, a multiview deep convolutional network processes four standard mammographic views.
5：Data Analysis Methods:
Involves evaluating performance metrics such as area under the curve (AUC), sensitivity, specificity, and cancer detection rates. Statistical comparisons between traditional and deep learning-based CAD systems are discussed.
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