<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sikaroudi, Milad</style></author><author><style face="normal" font="default" size="100%">Safarpoor, Amir</style></author><author><style face="normal" font="default" size="100%">Ghojogh, Benyamin</style></author><author><style face="normal" font="default" size="100%">Shafiei, Sobhan</style></author><author><style face="normal" font="default" size="100%">Crowley, Mark</style></author><author><style face="normal" font="default" size="100%">Tizhoosh, HR</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'20)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">digipath</style></keyword><keyword><style  face="normal" font="default" size="100%">digital pathology</style></keyword><keyword><style  face="normal" font="default" size="100%">histopathology</style></keyword><keyword><style  face="normal" font="default" size="100%">medical</style></keyword><keyword><style  face="normal" font="default" size="100%">representation learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://embs.papercept.net/conferences/scripts/rtf/EMBC20_ContentListWeb_1.html#moat2-15_02</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE Engineering in Medicine and Biology Society</style></publisher><pub-location><style face="normal" font="default" size="100%">42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'20)</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	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.&lt;br&gt;&amp;nbsp;
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