Although automated pathology classification using deep learning (DL) has proved to be predictively efficient, DL methods are found to be data and compute cost intensive. In this work, we aim to reduce DL training costs by pre-training a Resnet feature extractor using SimCLR contrastive loss for latent encoding of OCT images. We propose a novel active learning framework that identifies a minimal sub-sampled dataset containing the most uncertain OCT image samples using label propagation on the SimCLR latent encodings. The pre-trained Resnet model is then fine-tuned with the labelled minimal sub-sampled data and the underlying pathological sites are visually explained. Our framework identifies upto 2% of OCT images to be most uncertain that need prioritized specialist attention and that can fine-tune a Resnet model to achieve upto 97% classification accuracy. The proposed method can be extended to other medical images to minimize prediction costs.

中文翻译：

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SISE-PC：可解释病理学的半监督图像子采样

尽管已证明使用深度学习（DL）进行自动病理学分类具有预测性，但发现DL方法需要大量数据并且计算量很大。在这项工作中，我们旨在通过使用SimCLR对比损失对OCT图像进行潜在编码的Resnet特征提取器进行预训练来减少DL训练成本。我们提出了一种新颖的主动学习框架，该框架使用SimCLR潜在编码上的标签传播来识别包含最不确定的OCT图像样本的最小子抽样数据集。然后使用标记的最小子采样数据对预训练的Resnet模型进行微调，并从视觉上解释潜在的病理部位。我们的框架可识别最多2％的OCT图像，这些不确定性最需要专家的优先重视，并且可以微调Resnet模型以实现高达97％的分类精度。所提出的方法可以扩展到其他医学图像以最小化预测成本。