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Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest

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arxiv 1603.00275 v2 pith:T4R3MIT5 submitted 2016-03-01 cs.CV

Korsuk Sirinukunwattana , Josien P. W. Pluim , Hao Chen , Xiaojuan Qi , Pheng-Ann Heng , Yun Bo Guo , Li Yang Wang , Bogdan J. Matuszewski
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Elia Bruni Urko Sanchez Anton Böhm Olaf Ronneberger Bassem Ben Cheikh Daniel Racoceanu Philipp Kainz Michael Pfeiffer Martin Urschler David R. J. Snead Nasir M. Rajpoot
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classification cs.CV
keywords challengecoloncancercontestevaluationglandglandsglandular
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Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.

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  1. AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks

    cs.CV 2019-08 conditional novelty 6.0 of 10

    Per-pixel loss reweighting across stages, inspired by AdaBoost, improves gland segmentation accuracy over fixed-attention and non-boosted multi-stage baselines on a private dataset.

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