@conference {10.1007/978-3-031-73281-2_7,
	title = {TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image Classification: Leveraging Epistemic Uncertainty to Improve Model Performance},
	booktitle = {Simulation and Synthesis in Medical Imaging},
	year = {2024},
	pages = {69{\textendash}78},
	publisher = {Springer Nature Switzerland},
	organization = {Springer Nature Switzerland},
	address = {Cham},
	abstract = {The usage of medical image data for the training of large-scale machine learning approaches is particularly challenging due to its scarce availability and the costly generation of data annotations, typically requiring the engagement of medical professionals. The rapid development of generative models enables us to tackle this problem by generating large amounts of realistic synthetic data for the training process. However, randomly choosing synthetic samples, might not be an optimal strategy.},
	isbn = {978-3-031-73281-2},
	doi = {10.1007/978-3-031-73281-2_7},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-73281-2_7},
	author = {Niemeijer, Joshua and Jan Ehrhardt and Uzunova, Hristina and Heinz Handels},
	editor = {Fernandez, Virginia and Wolterink, Jelmer M. and Wiesner, David and Remedios, Samuel and Zuo, Lianrui and Casamitjana, Adri{\`a}}
}
