## 2 Related Works
### Addressing Data Scarcity in Medical Deep Learning Applications  
Training modern deep learning models increasingly requires large datasets, which are expensive to collect. Common strategies for handling limited training data rely on classical data augmentation techniques [11; 28], including geometric transformations (e.g., flipping, rotation) and color adjustments (e.g., channel shuffling, contrast enhancement). Automated approaches, such as RandAugment [29] and AugMix [30], dynamically apply a wide range of augmentations in a controlled and randomized manner, optimizing the augmentation process without requiring manual tuning of individual transformations. Image mixing methods, such as Mixup [31], CutMix [32], and Mosaic Augmentation [33], increase data diversity by combining multiple images according to predefined rules, further boosting the robustness of the model. [PERSON] et al. [14] extend image-wise mixing by extracting individual objects from one image and inserting them into another, thereby specifically increasing the variability of object classes.  
In addition to conventional data augmentation strategies, there is a growing interest in synthetic data generation across various domains, such as natural image processing [34; 35] or autonomous driving [36]. In the medical field, numerous studies leverage generative adversarial networks (GANs) [37] to generate synthetic data for various applications, such as breast cancer detection using synthetic X-ray images [38; 39; 40], and brain [13; 41; 42] and skin cancer [43; 44] classification and segmentation. Recently, diffusion-based models have gained attention in the medical field, with the first methods developed for generating training data [34; 45; 46] for multiple tasks.  
In the context of colony detection and segmentation, synthetic data have been leveraged to alleviate the shortage of labeled training data. For example, [PERSON] et al. [12] used a GP-WGAN [47] to generate synthetic bacterial colony patches, which were then overlaid onto background images for binary segmentation. While this approach enhanced realism through style transfer, it was limited by its inability to distinguish between different colony types.  
Similarly, [PERSON] et al. [3] utilized traditional computer vision techniques to segment colonies from real agar dish images, applying a Copy-Paste method to place colonies onto empty dishes. Afterwards, they performed a style transfer to generate more realistic images. Although this approach showed potential, it resulted in a lot of artifacts caused by inaccurate background textures and imprecise colony segmentation. Moreover, their method did not incorporate fine-tuning on real datasets, missing an opportunity for further model refinement.  
In summary, existing methods for colony generation are limited by artifact-prone generated images and the absence of fine-tuning on real data to improve model performance. Scalability also remains a challenge, as approaches using GANs or style transfer require significant computing resources. Our proposed SAM-based [15] Copy-Paste approach addresses these gaps by significantly reducing artifacts, improving realism, and providing a more computationally efficient alternative. Unlike previous methods, our approach is easily scalable for generating large datasets while also incorporating fine-tuning on real data, thereby achieving superior detection accuracy and bridging critical gaps in both performance and efficiency.