Faculty of Information Technology, Beijing University of Technology, Beijing, China.
Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Biomed Eng Online. 2021 Apr 23;20(1):39. doi: 10.1186/s12938-021-00877-5.
Glaucoma is one of the causes that leads to irreversible vision loss. Automatic glaucoma detection based on fundus images has been widely studied in recent years. However, existing methods mainly depend on a considerable amount of labeled data to train the model, which is a serious constraint for real-world glaucoma detection.
In this paper, we introduce a transfer learning technique that leverages the fundus feature learned from similar ophthalmic data to facilitate diagnosing glaucoma. Specifically, a Transfer Induced Attention Network (TIA-Net) for automatic glaucoma detection is proposed, which extracts the discriminative features that fully characterize the glaucoma-related deep patterns under limited supervision. By integrating the channel-wise attention and maximum mean discrepancy, our proposed method can achieve a smooth transition between general and specific features, thus enhancing the feature transferability.
To delimit the boundary between general and specific features precisely, we first investigate how many layers should be transferred during training with the source dataset network. Next, we compare our proposed model to previously mentioned methods and analyze their performance. Finally, with the advantages of the model design, we provide a transparent and interpretable transferring visualization by highlighting the key specific features in each fundus image. We evaluate the effectiveness of TIA-Net on two real clinical datasets and achieve an accuracy of 85.7%/76.6%, sensitivity of 84.9%/75.3%, specificity of 86.9%/77.2%, and AUC of 0.929 and 0.835, far better than other state-of-the-art methods.
Different from previous studies applied classic CNN models to transfer features from the non-medical dataset, we leverage knowledge from the similar ophthalmic dataset and propose an attention-based deep transfer learning model for the glaucoma diagnosis task. Extensive experiments on two real clinical datasets show that our TIA-Net outperforms other state-of-the-art methods, and meanwhile, it has certain medical value and significance for the early diagnosis of other medical tasks.
青光眼是导致不可逆转视力丧失的原因之一。近年来，基于眼底图像的自动青光眼检测已经得到了广泛的研究。然而，现有的方法主要依赖于大量的标记数据来训练模型，这对于实际的青光眼检测来说是一个严重的限制。
在本文中，我们介绍了一种迁移学习技术，该技术利用从类似眼科数据中学到的眼底特征来辅助诊断青光眼。具体来说，我们提出了一种用于自动青光眼检测的转移诱导注意网络（TIA-Net），该网络提取了在有限监督下充分表征青光眼相关深层模式的鉴别特征。通过整合通道注意和最大均值差异，我们的方法可以在一般特征和特定特征之间实现平滑过渡，从而提高特征的可转移性。
为了精确划定一般特征和特定特征的边界，我们首先研究了在训练过程中应该从源数据集网络中转移多少层。接下来，我们将我们的模型与之前提到的方法进行了比较，并分析了它们的性能。最后，利用模型设计的优势，我们通过突出每个眼底图像中的关键特定特征，提供了一种透明且可解释的转移可视化。我们在两个真实的临床数据集上评估了 TIA-Net 的有效性，达到了 85.7%/76.6%的准确率、84.9%/75.3%的灵敏度、86.9%/77.2%的特异性和 0.929 和 0.835 的 AUC，明显优于其他最先进的方法。
与之前将经典 CNN 模型应用于从非医疗数据集转移特征的研究不同，我们利用来自类似眼科数据集的知识，提出了一种基于注意力的深度迁移学习模型，用于青光眼诊断任务。在两个真实的临床数据集上的广泛实验表明，我们的 TIA-Net 优于其他最先进的方法，同时，它对于其他医学任务的早期诊断具有一定的医学价值和意义。
