论文标题
使用深度学习随着数据蒸馏和增强的深度学习，人类专家级脑肿瘤检测
Human-Expert-Level Brain Tumor Detection Using Deep Learning with Data Distillation and Augmentation
论文作者
论文摘要
深度学习（DL）在医学诊断中的应用通常受到两个问题的阻碍。首先，培训数据的量可能很少，因为它受到诊断为诊断病情的患者数量的限制。其次，训练数据可能会因各种类型的噪声而破坏。在这里，我们研究了磁共振光谱（MRS）数据的脑肿瘤检测问题，其中两种类型的问题都是突出的。为了克服这些挑战，我们提出了一种培训深层神经网络的新方法，该方法通过将一个类别的这些样本与来自同一和其他类别的类别的样本混合在一起来提高代表性的培训示例，并增加培训数据，以创建其他培训样本。我们证明，这种技术大大提高了性能，从而使我们的方法仅几千次培训示例就可以达到人类专家级别的准确性。有趣的是，网络学会依靠人类专家通常忽略的数据的特征，这为未来的研究提出了新的方向。
The application of Deep Learning (DL) for medical diagnosis is often hampered by two problems. First, the amount of training data may be scarce, as it is limited by the number of patients who have acquired the condition to be diagnosed. Second, the training data may be corrupted by various types of noise. Here, we study the problem of brain tumor detection from magnetic resonance spectroscopy (MRS) data, where both types of problems are prominent. To overcome these challenges, we propose a new method for training a deep neural network that distills particularly representative training examples and augments the training data by mixing these samples from one class with those from the same and other classes to create additional training samples. We demonstrate that this technique substantially improves performance, allowing our method to reach human-expert-level accuracy with just a few thousand training examples. Interestingly, the network learns to rely on features of the data that are usually ignored by human experts, suggesting new directions for future research.
