论文标题
通过自学学习有效评估有效的医疗图像评估
Efficient Medical Image Assessment via Self-supervised Learning
论文作者
论文摘要
高性能深度学习方法通常依赖于大型注释的培训数据集，由于医疗图像标签的高成本，在许多临床应用中很难获得。现有的数据评估方法通常需要事先了解标签，而这些标签是无法实现“知道要标记哪些数据”的目标。为此，我们制定并提出了一种新颖有效的数据评估策略，指数边缘奇异值（检查）分数，以根据通过自我监督学习（SSL）网络提取的有用的潜在表示，对未标记的医学图像数据进行排名。由SSL嵌入空间的理论含义激发，我们利用蒙版的自动编码器进行特征提取。此外，在排除数据集中的数据点之后，我们基于最大奇异值的边际变化来评估数据质量。我们对病理数据集进行了广泛的实验。我们的结果表明，我们提出的方法选择最有价值的数据的有效性和效率。
High-performance deep learning methods typically rely on large annotated training datasets, which are difficult to obtain in many clinical applications due to the high cost of medical image labeling. Existing data assessment methods commonly require knowing the labels in advance, which are not feasible to achieve our goal of 'knowing which data to label.' To this end, we formulate and propose a novel and efficient data assessment strategy, EXponentiAl Marginal sINgular valuE (EXAMINE) score, to rank the quality of unlabeled medical image data based on their useful latent representations extracted via Self-supervised Learning (SSL) networks. Motivated by theoretical implication of SSL embedding space, we leverage a Masked Autoencoder for feature extraction. Furthermore, we evaluate data quality based on the marginal change of the largest singular value after excluding the data point in the dataset. We conduct extensive experiments on a pathology dataset. Our results indicate the effectiveness and efficiency of our proposed methods for selecting the most valuable data to label.
