One-Vote Veto：面向小样本青光眼诊断的半监督学习
One-Vote Veto: Semi-Supervised Learning for Low-Shot Glaucoma Diagnosis
AI总结：
针对医学图像标注数据稀缺的问题，提出多任务Siamese网络实现小样本青光眼诊断，并设计One-Vote Veto自训练半监督策略提升准确率，在多数据集上验证了有效性与泛化性。
摘要：
卷积神经网络（CNNs）是一种基于眼底图像实现自动化青光眼诊断的极具潜力的技术，而眼底图像是眼科检查中的常规采集项目。然而，CNN通常需要大量标注良好的数据进行训练，这在许多生物医学图像分类应用中往往难以满足，尤其是当疾病较为罕见且专家标注成本高昂时。本文针对这一问题提出了两项贡献：（1）对传统Siamese网络（孪生网络）进行了扩展，提出了一种适用于标注数据有限且不均衡场景下小样本学习的训练方法；（2）提出了一种新颖的半监督学习策略，可利用额外的无标注训练数据进一步提升准确率。我们提出的多任务Siamese网络（MTSN）可适配任意骨干CNN，我们通过四种骨干CNN验证了该网络的性能：在训练数据有限的情况下，其准确率接近使用50倍规模数据集训练的骨干CNN的准确率。我们还提出了One-Vote Veto（OVV，一票否决）自训练方法，这是一种专为MTSN设计的半监督学习策略。通过同时考虑无标注训练数据的自预测结果和对比预测结果，OVV自训练可生成额外的伪标签，用于对预训练的MTSN进行微调。我们使用了一个跨度15年采集的包含66715张眼底照片的大型（不均衡）数据集开展了大量实验，结果验证了MTSN小样本学习以及结合OVV自训练的半监督学习的有效性。此外，我们还使用了三个在不同条件（不同相机、仪器、地点、人群）下采集的更小的临床眼底图像数据集，验证了所提方法的泛化能力。
英文摘要：
Convolutional neural networks (CNNs) are a promising technique for automated glaucoma diagnosis from images of the fundus, and these images are routinely acquired as part of an ophthalmic exam. Nevertheless, CNNs typically require a large amount of well-labeled data for training, which may not be available in many biomedical image classification applications, especially when diseases are rare and where labeling by experts is costly. This article makes two contributions to address this issue: (1) It extends the conventional Siamese network and introduces a training method for low-shot learning when labeled data are limited and imbalanced, and (2) it introduces a novel semi-supervised learning strategy that uses additional unlabeled training data to achieve greater accuracy. Our proposed multi-task Siamese network (MTSN) can employ any backbone CNN, and we demonstrate with four backbone CNNs that its accuracy with limited training data approaches the accuracy of backbone CNNs trained with a dataset that is 50 times larger. We also introduce One-Vote Veto (OVV) self-training, a semi-supervised learning strategy that is designed specifically for MTSNs. By taking both self-predictions and contrastive predictions of the unlabeled training data into account, OVV self-training provides additional pseudo labels for fine-tuning a pre-trained MTSN. Using a large (imbalanced) dataset with 66,715 fundus photographs acquired over 15 years, extensive experimental results demonstrate the effectiveness of low-shot learning with MTSN and semi-supervised learning with OVV self-training. Three additional, smaller clinical datasets of fundus images acquired under different conditions (cameras, instruments, locations, populations) are used to demonstrate the generalizability of the proposed methods.
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