一种用于活检图像早期乳腺癌检测的人工智能模型
An Artificial Intelligence Model for Early Stage Breast Cancer Detection from Biopsy Images
AI总结：
本文提出一种基于卷积神经网络的人工智能模型，利用活检图像区分良恶性并亚分类乳腺癌类型，实验在准确率、精确率、召回率和F1分数上优于现有方案，助力临床诊断。
摘要：
准确识别乳腺癌类型在指导治疗决策和改善患者预后方面起着关键作用。本文提出了一种基于人工智能的工具，旨在利用组织病理学活检图像辅助识别乳腺癌类型。传统上，被检测出患有乳腺癌的女性需要接受额外检查以确定癌症类型，从而给予必要的治疗。这些检查不仅具有侵入性，而且会延误治疗的开始并增加患者负担。所提出的模型采用卷积神经网络（CNN）架构来区分良性和恶性组织，并对乳腺癌类型进行准确的亚分类。通过对图像进行预处理以减少噪声并增强特征，该模型实现了可靠的分类性能水平。在此类数据集上的实验结果表明了该模型的有效性，在准确率、精确率、召回率和F1分数方面优于若干现有解决方案。该研究强调了深度学习技术在临床诊断中的潜力，并提供了一种有前景的工具来辅助病理学家进行乳腺癌分类。
英文摘要：
Accurate identification of breast cancer types plays a critical role in guiding treatment decisions and improving patient outcomes. This paper presents an artificial intelligence enabled tool designed to aid in the identification of breast cancer types using histopathological biopsy images. Traditionally additional tests have to be done on women who are detected with breast cancer to find out the types of cancer it is to give the necessary cure. Those tests are not only invasive but also delay the initiation of treatment and increase patient burden. The proposed model utilizes a convolutional neural network (CNN) architecture to distinguish between benign and malignant tissues as well as accurate subclassification of breast cancer types. By preprocessing the images to reduce noise and enhance features, the model achieves reliable levels of classification performance. Experimental results on such datasets demonstrate the model's effectiveness, outperforming several existing solutions in terms of accuracy, precision, recall, and F1-score. The study emphasizes the potential of deep learning techniques in clinical diagnostics and offers a promising tool to assist pathologists in breast cancer classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
