AI模型通过病理切片图早期精准分类乳腺癌类型，减少侵入性检测。
An Artificial Intelligence Model for Early Stage Breast Cancer Detection from Biopsy Images
- 用CNN分析病理图像，自动区分良恶性及癌种类型。
- 在多个数据集上准确率、召回率等指标优于现有方法。
- 适合临床辅助诊断，帮助病理医生快速决策。
准确识别乳腺癌类型对指导治疗和改善患者预后至关重要。本文提出一种基于人工智能的工具，用于通过组织病理学活检图像辅助识别乳腺癌类型。传统上，确诊乳腺癌后还需进行额外侵入性检测以确定癌症亚型，耗时且增加患者负担。该模型采用卷积神经网络（CNN）架构，可区分良性与恶性组织，并实现乳腺癌类型的精确细分。通过对图像进行去噪和特征增强预处理，模型达到可靠的分类性能。实验结果表明，该模型在多个数据集上的准确率、精确率、召回率和F1分数均优于多种现有方法。研究强调了深度学习在临床诊断中的潜力，为病理科医生提供了一项有前景的辅助工具。
原文摘要 · Abstract (English)
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.
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