Colorectal Gland Segmentation:
6 papers with code • 1 benchmarks • 2 datasets
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear.
Machine learning approach for segmenting glands in colon histology images using local intensity and texture features
A multilevel random forest technique in a hierarchical way is proposed.
This study is focused on histopathology image analysis applications for which it is desirable that the arbitrary global orientation information of the imaged tissues is not captured by the machine learning models.
Although recent works in semi-supervised learning (SemiSL) have accomplished significant success in natural image segmentation, the task of learning discriminative representations from limited annotations has been an open problem in medical images.