Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study

• URL: http://arxiv.org/abs/2005.08629v1
• Date: Sun, 10 May 2020 21:27:38 GMT
• Title: Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
• Authors: Milad Sikaroudi, Amir Safarpoor, Benyamin Ghojogh, Sobhan Shafiei, Mark Crowley, H.R. Tizhoosh
• Abstract summary: 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. We achieved high accuracy and generalization when the learned representations were applied to two different pathology datasets.
• Score: 6.762603053858596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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