用生成模型扩充小样本医疗图像数据，提升分类准确率。
Enhancing Image Classification in Small and Unbalanced Datasets through Synthetic Data Augmentation
- 用类特定VAE和潜空间插值生成真实感合成数据
- 小样本下未充分代表类别的准确率提升18%以上
- 适合医疗图像等小而不平衡数据集的场景
医学图像分类在标注数据少且类别严重不平衡的场景下极具挑战。由于数据获取困难，尤其对少数类而言，本文提出一种基于类特定变分自编码器（VAEs）和潜空间插值的新型合成数据增强策略，以填补特征空间空缺，缓解数据稀缺与类别不平衡问题。通过在每个类内进行潜变量插值生成多样化的合成图像，丰富训练集，增强模型泛化能力与诊断准确性。该方法在包含321张图像的小型数据集上测试，用于评估胃镜图像清洁度的自动评估系统。结合真实与合成数据后，最困难的未充分代表类别准确率提升超过18%，全局准确率和精确率分别提升6%。该策略不仅改善了少数类表现，也整体提升了模型性能。
原文摘要 · Abstract (English)
Accurate and robust medical image classification is a challenging task, especially in application domains where available annotated datasets are small and present high imbalance between target classes. Considering that data acquisition is not always feasible, especially for underrepresented classes, our approach introduces a novel synthetic augmentation strategy using class-specific Variational Autoencoders (VAEs) and latent space interpolation to improve discrimination capabilities. By generating realistic, varied synthetic data that fills feature space gaps, we address issues of data scarcity and class imbalance. The method presented in this paper relies on the interpolation of latent representations within each class, thus enriching the training set and improving the model's generalizability and diagnostic accuracy. The proposed strategy was tested in a small dataset of 321 images created to train and validate an automatic method for assessing the quality of cleanliness of esophagogastroduodenoscopy images. By combining real and synthetic data, an increase of over 18\% in the accuracy of the most challenging underrepresented class was observed. The proposed strategy not only benefited the underrepresented class but also led to a general improvement in other metrics, including a 6\% increase in global accuracy and precision.
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