Ultrasound Image Classification using ACGAN with Small Training Dataset
This addresses the bottleneck of small datasets in medical imaging for researchers and practitioners, but it is incremental as it applies an existing method to a specific domain.
The authors tackled the problem of limited labeled data for training deep learning models in ultrasound image classification by using an Auxiliary Classifier Generative Adversarial Network (ACGAN) to combine data augmentation and transfer learning, demonstrating its effectiveness on a breast ultrasound dataset.
B-mode ultrasound imaging is a popular medical imaging technique. Like other image processing tasks, deep learning has been used for analysis of B-mode ultrasound images in the last few years. However, training deep learning models requires large labeled datasets, which is often unavailable for ultrasound images. The lack of large labeled data is a bottleneck for the use of deep learning in ultrasound image analysis. To overcome this challenge, in this work we exploit Auxiliary Classifier Generative Adversarial Network (ACGAN) that combines the benefits of data augmentation and transfer learning in the same framework. We conduct experiment on a dataset of breast ultrasound images that shows the effectiveness of the proposed approach.