Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest

In this paper we present the specifics of the ExB algorithm which obtained the 2nd position in the Gland Segmentation Challenge (GlaS) organised at MICCAI 2015. Our method is based on a Multi-Path Convolutional Neural Network  architecture for image segmentation. A major innovation of our model is the specialized border identification network which improves accuracy at the borders of glands and substantially improve the overall segmentation accuracy.

Sirinukunwattana, Korsuk, Josien PW Pluim, Hao Chen, Xiaojuan Qi, Pheng-Ann Heng, Yun Bo Guo, Li Yang Wang, Bogdan J Matuszewski, Elia Bruni, Urko Sanchez, Anton Böhm, Olaf Ronneberger, Bassem Ben Cheikh, Daniel Racoceanu, Philipp Kainz, Michael Pfeiffer, Martin Urschler, David RJ Snead, Nasir M Rajpoot (2016): Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest

Multilingual Singledocument Summarization and Multilingual Multi-document Summarization

This work presents our state of the art multilingual text summarizer capable of single as well as multi-document text summarization. The algorithm is based on repeated application of TextRank on a sentence similarity graph, a bag of words model for sentence similarity and a number of linguistic pre- and post-processing steps using standard NLP tools. We submitted this algorithm for two different tasks of the MultiLing 2015 summarization challenge: Multilingual Singledocument Summarization and Multilingual Multi-document Summarization.

Thomas, Stefan, Christian Beutenmüller, Xose de la Puente, Robert Remus, and Stefan Bordag (2015): 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, page 260