Abstract
Deep neural networks have reached remarkable achievements in medical image processing tasks, specifically classifying and detecting various diseases. However, when confronted with limited data, these networks face a critical vulnerability, often succumbing to overfitting by excessively memorizing the limited information available. This work addresses the challenge mentioned above by improving the supervised contrastive learning method to reduce the impact of false positives. Unlike most existing methods that rely predominantly on fully supervised learning, our approach leverages the advantages of self-supervised learning in conjunction with employing the available labeled data. We evaluate our method on the BreakHis dataset, which consists of breast cancer histopathology images, and demonstrate an increase in classification accuracy by 1.45% at the image level and 1.42% at the patient level compared to the state-of-the-art method. This improvement corresponds to 93.63% absolute accuracy, highlighting our approach's effectiveness in leveraging data properties to learn more appropriate representation space.
Abstract (translated)
深度神经网络在医学图像处理任务中取得了显著的成就，尤其是在分类和检测各种疾病方面。然而，当面对有限数据时，这些网络面临着一个关键的漏洞，常常通过过度依赖有限信息而陷入过拟合。本文通过改进有监督对比学习方法来应对上述挑战。与大多数现有方法主要依赖完全监督学习不同，我们的方法利用自监督学习的优势，并利用现有标记数据。我们在BreakHis数据集上评估我们的方法，该数据集包含乳腺癌 histopathology 图像，证明了在图像级别和患者级别，分类准确率分别比最先进的方法提高了1.45%和1.42%。这种提高相当于93.63%的绝对准确率，强调了我们的方法利用数据属性来学习更合适的表示空间的有效性。
URL
https://arxiv.org/abs/2405.03642
