IEEE/ACM Trans Comput Biol Bioinform. 2024 Jul-Aug;21(4):715-724. doi: 10.1109/TCBB.2023.3282226. Epub 2024 Aug 9.
As a high mortality disease, cancer seriously affects people's life and well-being. Reliance on pathologists to assess disease progression from pathological images is inaccurate and burdensome. Computer aided diagnosis (CAD) system can effectively assist diagnosis and make more credible decisions. However, a large number of labeled medical images that contribute to improve the accuracy of machine learning algorithm, especially for deep learning in CAD, are difficult to collect. Therefore, in this work, an improved few-shot learning method is proposed for medical image recognition. In addition, to make full use of the limited feature information in one or more samples, a feature fusion strategy is involved in our model. On the dataset of BreakHis and skin lesions, the experimental results show that our model achieved the classification accuracy of 91.22% and 71.20% respectively when only 10 labeled samples are given, which is superior to other state-of-the-art methods.
作为一种高死亡率的疾病，癌症严重影响人们的生活和健康。依赖病理学家根据病理图像评估疾病进展情况既不准确又繁琐。计算机辅助诊断（CAD）系统可以有效地协助诊断并做出更可信的决策。然而，大量有助于提高机器学习算法准确性的标记医疗图像，尤其是 CAD 中的深度学习，难以收集。因此，在这项工作中，提出了一种改进的小样本学习方法，用于医学图像识别。此外，为了充分利用一个或多个样本中的有限特征信息，我们的模型中涉及了一种特征融合策略。在 BreakHis 和皮肤病变数据集上的实验结果表明，当只给定 10 个标记样本时，我们的模型分别实现了 91.22%和 71.20%的分类准确率，优于其他最先进的方法。
