乳腺肿块检测和分类中的迁移学习

3区 计算机科学 Q1 Computer Science Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-08-06 DOI:10.1007/s12652-024-04835-6
Marya Ryspayeva, Alessandro Bria, Claudio Marrocco, Francesco Tortorella, Mario Molinara
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引用次数: 0

摘要

Covid-19感染影响了全球乳腺癌的筛查率,原因在于检疫措施、常规程序的减少以及早期诊断的延迟,造成了高死亡率风险和疾病的严重性。X 射线乳房 X 线照相术是诊断乳腺癌早期征兆的金标准,人工智能可检测可疑病灶并对其进行恶性分类。本文旨在研究大规模 OPTIMAM 数据集中的肿块检测和分类,该数据集包含 6000 个病例,并提取了 3524 幅 Hologic 生产商生产的乳房 X 光照片中的肿块图像。检测步骤的方法是训练由 ResNet50、ResNet101 和 ResNet152 骨干组成的 RetinaNet 架构,并通过 ImageNet 和 COCO 权重以及从头开始进行三种类型的初始化。对数据集进行预处理后,生成两种类型的输入,即整个乳房 X 光照片和乳房 X 光补丁,分别称为第一种方法和第二种方法。结果显示,在第一种方法中,使用 ImageNet 和 COCO 权重的 ResNet50 骨干 RetinaNet 和使用相同权重的 ResNet152 的真阳性率分别为 0.91 和 0.78。相比之下,在第二种方法中,采用 ImageNet 权重的 ResNet152 的真阳性率为 0.88,假阳性率为 0.78。在分类步骤中,通过添加 L2- 规则化和类权重,对迁移学习方法进行了微调,以平衡数据集中的类分布。
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Transfer learning in breast mass detection and classification

Covid-19 infection influenced the screening test rate of breast cancer worldwide due to the quarantine measures, routine procedures reduction, and delay of early diagnosis, causing high mortality risk and severity of the disease. X-ray mammography is the gold standard for diagnosing early signs of breast cancer, and Artificial Intelligence enables the detection of suspicious lesions and classifying them in terms of malignancy. This paper aimed to investigate mass detection and classification in a large-scale OPTIMAM dataset with 6000 cases and extracted 3524 images with masses in the mammograms of the Hologic manufacturer. The methodology of the detection step is to train the RetinaNet architecture of ResNet50, ResNet101, and ResNet152 backbones with three types of initializations by ImageNet and COCO weights and from scratch. The dataset was pre-processed to generate two types of input with entire mammograms and patches, which are stated as the first and the second approaches. The results show that in the first approach, RetinaNet of ResNet50 backbone with ImageNet and COCO weights and ResNet152 with the same weights performed 0.91 True Positive Rate at 0.78 False Positive Per Image, respectively. In contrast, in the second approach, ResNet152 with ImageNet weights reached 0.88 TPR at 0.78 FPPI. In the classification step, the Transfer Learning approach was applied with fine-tuning by adding L2-regularization and class weights to balance class distribution in the datasets.

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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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