The effectiveness of Deep Neural Networks (DNNs) heavily relies on the abundance and accuracy of available training data. However, collecting and annotating data on a large scale is often both costly and time-intensive, particularly in medical cases where practitioners are already occupied with their duties. Moreover, ensuring that the model remains robust across various scenarios of image capture is crucial in medical domains, especially when dealing with ultrasound images that vary based on the settings of different devices and the manual operation of the transducer. To address this challenge, we introduce a novel pipeline called MEDDAP, which leverages Stable Diffusion (SD) models to augment existing small datasets by automatically generating new informative labeled samples. Pretrained checkpoints for SD are typically based on natural images, and training them for medical images requires significant GPU resources due to their heavy parameters. To overcome this challenge, we introduce USLoRA (Ultrasound Low-Rank Adaptation), a novel fine-tuning method tailored specifically for ultrasound applications. USLoRA allows for selective fine-tuning of weights within SD, requiring fewer than 0.1\% of parameters compared to fully fine-tuning only the UNet portion of SD. To enhance dataset diversity, we incorporate different adjectives into the generation process prompts, thereby desensitizing the classifiers to intensity changes across different images. This approach is inspired by clinicians' decision-making processes regarding breast tumors, where tumor shape often plays a more crucial role than intensity. In conclusion, our pipeline not only outperforms classifiers trained on the original dataset but also demonstrates superior performance when encountering unseen datasets. The source code is available at https://github.com/yasamin-med/MEDDAP.

翻译：摘要：深度神经网络（DNN）的有效性高度依赖于可用训练数据的丰富性和准确性。然而，大规模收集和标注数据往往成本高昂且耗时，尤其是在医学案例中，从业人员已忙于本职工作。此外，确保模型在多种图像捕获场景下保持鲁棒性至关重要，尤其是在处理因不同设备设置和探头手动操作而变化的超声图像时。为应对这一挑战，我们提出了一种名为MEDDAP的新型流水线，该流水线利用Stable Diffusion（SD）模型通过自动生成新的信息性标注样本来扩展现有小数据集。SD的预训练检查点通常基于自然图像，而对其在医学图像上进行训练因其参数量庞大而需要大量GPU资源。为解决此问题，我们引入了USLoRA（超声低秩适应），这是一种专为超声应用定制的新型微调方法。USLoRA允许对SD中的权重进行选择性微调，与仅对SD的UNet部分进行全参数微调相比，所需参数少于0.1%。为增强数据集多样性，我们在生成过程的提示中融入不同形容词，从而降低分类器对不同图像强度变化的敏感度。该方法受临床医生对乳腺肿瘤决策过程的启发，其中肿瘤形状通常比强度发挥更关键的作用。总之，我们的流水线不仅优于在原始数据集上训练的分类器，而且在遇到未见过的数据集时表现出更优性能。源代码可在https://github.com/yasamin-med/MEDDAP获取。