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.
Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest
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abstract
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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AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks
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.