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使用微调后的MobileNet对乳腺病变的动态对比增强磁共振成像（DCE-MRI）数据进行分类

Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet.

作者信息

Wang Long, Zhang Ming, He Guangyuan, Shen Dong, Meng Mingzhu

机构信息

Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213164, China.

出版信息

Diagnostics (Basel). 2023 Mar 11;13(6):1067. doi: 10.3390/diagnostics13061067.

DOI:10.3390/diagnostics13061067
PMID:36980377
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10047403/
Abstract

It is crucial to diagnose breast cancer early and accurately to optimize treatment. Presently, most deep learning models used for breast cancer detection cannot be used on mobile phones or low-power devices. This study intended to evaluate the capabilities of MobileNetV1 and MobileNetV2 and their fine-tuned models to differentiate malignant lesions from benign lesions in breast dynamic contrast-enhanced magnetic resonance images (DCE-MRI).

摘要

早期准确诊断乳腺癌对于优化治疗至关重要。目前，大多数用于乳腺癌检测的深度学习模型无法在手机或低功耗设备上使用。本研究旨在评估MobileNetV1和MobileNetV2及其微调模型在乳腺动态对比增强磁共振成像（DCE-MRI）中区分恶性病变和良性病变的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/1645725fdcae/diagnostics-13-01067-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/f30b78d6fdaf/diagnostics-13-01067-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/ed1d62c07edc/diagnostics-13-01067-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/99f015a281bb/diagnostics-13-01067-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/6092ee2842b7/diagnostics-13-01067-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/828948f019af/diagnostics-13-01067-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/4fb8ec9ee9f7/diagnostics-13-01067-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/cfa17c83ae42/diagnostics-13-01067-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/fac3fbb36a2f/diagnostics-13-01067-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/1645725fdcae/diagnostics-13-01067-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/f30b78d6fdaf/diagnostics-13-01067-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/ed1d62c07edc/diagnostics-13-01067-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/99f015a281bb/diagnostics-13-01067-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/6092ee2842b7/diagnostics-13-01067-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/828948f019af/diagnostics-13-01067-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/4fb8ec9ee9f7/diagnostics-13-01067-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/cfa17c83ae42/diagnostics-13-01067-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/fac3fbb36a2f/diagnostics-13-01067-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33de/10047403/1645725fdcae/diagnostics-13-01067-g009.jpg

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A new detection model of microaneurysms based on improved FC-DenseNet.基于改进型 FC-DenseNet 的微动脉瘤新检测模型。
Sci Rep. 2022 Jan 19;12(1):950. doi: 10.1038/s41598-021-04750-2.
3
Artificial intelligence for breast cancer analysis: Trends & directions.用于乳腺癌分析的人工智能：趋势与方向。
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4
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