… of glands in tissue of benign and malignant colorectal cancer, which was developed to
participate in the GlaS@ MICCAI2015 colon gland … While the first classifier separates glands from …

… colon gland segmentation challenge, we present a learning-based algorithm to segment
glands in … The CNN predictions are then regularized using a figure-ground segmentation based …

… of gland … in gland segmentation lead to different errors. For example, poorly differentiated
adenocarcinoma includes signet ring cell carcinoma, which occurs in gastric cancer and colon …

… image features and use regularized machine-learning … of gland morphology have never
been reported in colon cancer … model could capture these variations precisely. These findings …

… in colon cancer grading. We propose an approach for segmentation of glands in colon
cancer histopathology images,which bases on the characteristics of lumens and rough gland …

… gland (colonic crypt) found in the epithelial layer of the colon, … the inner surface of the colon
into the underlying connective … There are millions of glands in the human colon. Intestinal …

… First, we pre-process the images to get rid of the variation in … loss, a binary cross-entropy
contour loss and L2 regularization … malignant colon glands using Gland- and Stroma-approach. …

… Colon gland images contain rich information about the patient’s condition and the …
However, they always fail to capture the complex representation of gland images and bring the …

… were regularized using a total variation-based figure-ground segmentation. In Ref. [12], an
encoder-decoder network, SegNet was introduced where the decoder used the indices of the …

… the understanding of morphology and variation for the region of interest. … is the anatomy of
the colonic glands with their architectural … Dropout regularization was added to reduce network …