@conference {sikaroudi2020supervision,
	title = {Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study},
	booktitle = {International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}20)},
	year = {2020},
	publisher = {IEEE Engineering in Medicine and Biology Society},
	organization = {IEEE Engineering in Medicine and Biology Society},
	address = {42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}20)},
	abstract = {
	As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks have boosted the performance of representation learning, the need for a large set of labeled data limits the application of such methods. As an example, high-quality delineations of regions of interest in the field of pathology is a tedious and time-consuming task due to the large image dimensions. In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning. We investigated the notion of similarity and dissimilarity in pathology whole-slide images and compared different setups from unsupervised and semi-supervised to supervised learning in our experiments. Additionally, different approaches were tested, applying few-shot learning on two publicly available pathology image datasets. We achieved high accuracy and generalization when the learned representations were applied to two different pathology datasets.\&nbsp;



	\&nbsp;

},
	keywords = {digipath, digital pathology, histopathology, medical, representation learning},
	url = {https://embs.papercept.net/conferences/scripts/rtf/EMBC20_ContentListWeb_1.html$\#$moat2-15_02},
	author = {Sikaroudi, Milad and Safarpoor, Amir and Ghojogh, Benyamin and Shafiei, Sobhan and Crowley, Mark and Tizhoosh, HR}
}
