Q1. What are the contributions in "Gland segmentation in colon histology images: the glas challenge contest" ?
This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest ( GlaS ) held at MICCAI ’ 2015.
Gland segmentation in colon histology images: The GlaS challenge contest
Korsuk Sirinukunwattana,Josien P. W. 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,Philipp Kainz,Michael Pfeiffer,Martin Urschler,David Snead,Nasir M. Rajpoot +19 more
TLDR
An overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015 is provided, along with the method descriptions and evaluation results from the top performing methods.About:
This article is published in Medical Image Analysis.The article was published on 2017-01-01 and is currently open access. It has received 574 citations till now.read more
Figures
Table 1: Details of the dataset. 
Figure 3: Performance scores achieved by different entries on the combined test data. Evaluation is conducted on three subsets of the data: (1st row) the whole test data, (2nd row) benign, and (3rd row) malignant. 
Table 3: Ranking results of the entries when the two parts of test data are combined. Two set of ranking scheme are considered: a) F1score + Diceobj+Hobj and b) F1score + ARI + Hobj. In addition to the evaluation on the whole test data (overall), the entries are evaluated on a subset of the data according to the histologic labels, i.e. benign and malignant. 
Figure 1: Example images of different histologic grades in the dataset: (a) benign and (b) malignant. 
Figure A.5: The ranking results from the GlaS Challenge Contest. 
Table 2: Summary results. The evaluation is carried out according to the challenge criteria described in Section 6. A ranking score is assigned to each algorithm according to its performance in each evaluation measure, obtained from each test part. The entries are listed in a descending order based on their rank sum
Citations
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Generative adversarial network in medical imaging: A review.
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MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation.
Nabil Ibtehaz,M. Sohel Rahman +1 more
TL;DR: This work develops a novel architecture, MultiResUNet, as the potential successor to the U-Net architecture, and tests and compared it with the classical U- net on a vast repertoire of multimodal medical images.
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Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks
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Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Kaustav Bera,Kurt A. Schalper,David L. Rimm,Vamsidhar Velcheti,Anant Madabhushi,Anant Madabhushi +5 more
TL;DR: A broad framework is provided for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development, and some of the challenges relating to the use of AI are discussed, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies.
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A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology
TL;DR: A large publicly accessible data set of hematoxylin and eosin (H&E)-stained tissue images with more than 21000 painstakingly annotated nuclear boundaries is introduced, whose quality was validated by a medical doctor.
References
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ImageNet Classification with Deep Convolutional Neural Networks
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U-Net: Convolutional Networks for Biomedical Image Segmentation
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