ITIS Software, University of Málaga, C/ Arquitecto Francisco Peñalosa, 18, 29010 Malaga, Spain.
Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, Avenida Severo Ochoa, 35, 29590 Malaga, Spain.
Sensors (Basel). 2024 Jun 11;24(12):3777. doi: 10.3390/s24123777.
Breast cancer is the second most common cancer worldwide, primarily affecting women, while histopathological image analysis is one of the possibile methods used to determine tumor malignancy. Regarding image analysis, the application of deep learning has become increasingly prevalent in recent years. However, a significant issue is the unbalanced nature of available datasets, with some classes having more images than others, which may impact the performance of the models due to poorer generalizability. A possible strategy to avoid this problem is downsampling the class with the most images to create a balanced dataset. Nevertheless, this approach is not recommended for small datasets as it can lead to poor model performance. Instead, techniques such as data augmentation are traditionally used to address this issue. These techniques apply simple transformations such as translation or rotation to the images to increase variability in the dataset. Another possibility is using generative adversarial networks (GANs), which can generate images from a relatively small training set. This work aims to enhance model performance in classifying histopathological images by applying data augmentation using GANs instead of traditional techniques.
乳腺癌是全球第二大常见癌症，主要影响女性，而组织病理学图像分析是确定肿瘤恶性程度的可能方法之一。在图像分析方面，深度学习的应用近年来变得越来越流行。然而，一个重要的问题是可用数据集的不平衡性质，某些类别的图像比其他类别的多，这可能会由于较差的泛化能力而影响模型的性能。一种可能的策略是对具有最多图像的类进行下采样，以创建一个平衡的数据集。然而，对于小数据集来说，这种方法并不推荐，因为它会导致模型性能不佳。相反，传统上使用数据增强技术来解决这个问题。这些技术对图像应用简单的变换，如平移或旋转，以增加数据集的可变性。另一种可能性是使用生成对抗网络 (GANs)，它可以从小的训练集中生成图像。这项工作旨在通过使用 GANs 而不是传统技术进行数据增强来提高模型在分类组织病理学图像方面的性能。
