Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms.
Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms.
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DOI：
10.1088/1361-6560/aa93d4
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发表时间：
2017-11-10
影响因子：
3.5
通讯作者：
Richter CD
中科院分区：
文献类型：
--
作者：
Samala RK;Chan HP;Hadjiiski LM;Helvie MA;Cha KH;Richter CD
深度卷积神经网络（DCNN）中的迁移学习是其应用于医学成像任务的重要一步。我们提出了一种多任务迁移学习DCNN，其目的是通过监督训练将从非医学图像中学到的“知识”转化为医学诊断任务，并通过同时学习辅助任务来提高DCNN的泛化能力。我们研究了这种方法的一个重要应用：恶性和良性乳腺肿块的分类。经IRB批准，从我们的患者档案中收集了数字化屏幕胶片乳腺X线摄影（SFM）和数字化乳腺X线摄影（DM），并从筛查乳腺X线摄影数字数据库中获得了额外的SFM。数据集包括2，242个视图，2，454个肿块（1，057个恶性，1，397个良性）。在单任务迁移学习中，DCNN在SFM上进行了训练和测试。在多任务迁移学习中，使用SFM和DM来训练DCNN，然后在SFM上进行测试。使用训练集的N折交叉验证进行训练和参数优化。在独立测试集上，发现多任务迁移学习DCNN的性能显著（p=0.007）高于单任务迁移学习DCNN。这项研究表明，当来自单一模态的训练样本有限时，多任务迁移学习可能是在医学成像应用中训练DCNN的有效方法。
Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aims of translating the ‘knowledge’ learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With IRB approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2,242 views with 2,454 masses (1,057 malignant, 1,397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multitask transfer learning DCNN was found to have significantly (p=0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multitask transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.
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影响因子：
3.8
作者：
Samala, Ravi K.;Chan, Heang-Ping;Cha, Kenny
通讯作者：
Cha, Kenny
影响因子：
1.8
作者：
Metz, CE;Pan, XC
通讯作者：
Pan, XC
影响因子：
10.6
作者：
Shin HC;Roth HR;Gao M;Lu L;Xu Z;Nogues I;Yao J;Mollura D;Summers RM
通讯作者：
Summers RM
影响因子：
10.6
作者：
Tajbakhsh, Nima;Shin, Jae Y.;Liang, Jianming
通讯作者：
Liang, Jianming
DOI：
10.1007/978-1-4615-5529-2_5
发表时间：
1998-01-01
期刊：
LEARNING TO LEARN
影响因子：
--
作者：
Caruana, R
通讯作者：
Caruana, R