  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.
