Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning.
Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning.
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DOI：
10.3390/cancers13215368
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发表时间：
2021-10-26
期刊：
影响因子：
5.2
通讯作者：
Tsuneki M
中科院分区：
文献类型：
--
作者：
Kanavati F;Tsuneki M
在这项研究中，我们使用迁移学习和弱监督学习训练了深度学习模型，用于对全切片图像（WSIs）中的乳腺浸润性导管癌（IDC）进行分类。我们在四个测试集上评估了模型：一个活检（n = 522）和三个手术（n = 1129），AUC范围为0.95至0.99。我们还将训练模型与现有的预训练模型在不同器官上进行了比较，用于腺癌分类，尽管腺癌与IDC表现出一些结构相似性，但它们在0.66至0.89的范围内实现了较低的AUC性能。因此，对乳腺IDC训练集进行微调有利于提高性能。结果表明，这种模型的潜在用途，以帮助病理学家在临床实践中。浸润性导管癌（IDC）是最常见的乳腺癌形式。对于乳腺癌的非手术诊断，近年来，粗针穿刺活检已被广泛用于评估组织病理学特征，因为它可以提供IDC和良性病变之间的明确诊断（例如，纤维腺瘤），而且具有成本效益。由于其广泛的使用，它可能会受益于使用基于AI的工具来帮助病理学家进行病理诊断工作流程。在本文中，我们训练浸润性导管癌（IDC）的全载玻片图像（WSI）分类模型，使用迁移学习和弱监督学习。我们在一个空芯针穿刺活检（n = 522）测试集以及三个手术测试集（n = 1129）上评估了模型，获得了0.95-0.98范围内的ROC AUC。有希望的结果表明，在临床实践中，应用这些模型作为诊断辅助工具的病理学家的潜力。
In this study, we have trained deep learning models using transfer learning and weakly-supervised learning for the classification of breast invasive ductal carcinoma (IDC) in whole slide images (WSIs). We evaluated the models on four test sets: one biopsy (n = 522) and three surgical (n = 1129) achieving AUCs in the range 0.95 to 0.99. We have also compared the trained models to existing pre-trained models on different organs for adenocarcinoma classification and they have achieved lower AUC performances in the range 0.66 to 0.89 despite adenocarcinoma exhibiting some structural similarity to IDC. Therefore, performing fine-tuning on the breast IDC training set was beneficial for improving performance. The results demonstrate the potential use of such models to aid pathologists in clinical practice. Invasive ductal carcinoma (IDC) is the most common form of breast cancer. For the non-operative diagnosis of breast carcinoma, core needle biopsy has been widely used in recent years for the evaluation of histopathological features, as it can provide a definitive diagnosis between IDC and benign lesion (e.g., fibroadenoma), and it is cost effective. Due to its widespread use, it could potentially benefit from the use of AI-based tools to aid pathologists in their pathological diagnosis workflows. In this paper, we trained invasive ductal carcinoma (IDC) whole slide image (WSI) classification models using transfer learning and weakly-supervised learning. We evaluated the models on a core needle biopsy (n = 522) test set as well as three surgical test sets (n = 1129) obtaining ROC AUCs in the range of 0.95–0.98. The promising results demonstrate the potential of applying such models as diagnostic aid tools for pathologists in clinical practice.
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影响因子：
4.6
作者：
Kanavati F;Toyokawa G;Momosaki S;Takeoka H;Okamoto M;Yamazaki K;Takeo S;Iizuka O;Tsuneki M
通讯作者：
Tsuneki M
影响因子：
4.6
作者：
Kanavati, Fahdi;Toyokawa, Gouji;Tsuneki, Masayuki
通讯作者：
Tsuneki, Masayuki
影响因子：
3.9
作者：
Hameed, Zabit;Zahia, Sofia;Maria Vanegas, Ana
通讯作者：
Maria Vanegas, Ana
DOI：
10.1093/bioinformatics/btw252
发表时间：
2016-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Kraus OZ;Ba JL;Frey BJ
通讯作者：
Frey BJ
影响因子：
4.6
作者：
Gertych, Arkadiusz;Swiderska-Chadaj, Zaneta;Knudsen, Beatrice S.
通讯作者：
Knudsen, Beatrice S.