Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Cutting-plane training of structural SVMs
Machine Learning
Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Pattern recognition in histopathological images: an ICPR 2010 contest
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
A classification scheme for lymphocyte segmentation in H&E stained histology images
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Identifying cells in histopathological images
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Lymphocyte segmentation using the transferable belief model
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Counting lymphocytes in histopathology images using connected components
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Cell segmentation using coupled level sets and graph-vertex coloring
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Unsupervised cell identification on multidimensional X-ray fluorescence datasets
ACM SIGGRAPH 2013 Posters
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Cell detection in microscopy images is an important step in the automation of cell based-experiments. We propose a machine learning-based cell detection method applicable to different modalities. The method consists of three steps: first, a set of candidate cell-like regions is identified. Then, each candidate region is evaluated using a statistical model of the cell appearance. Finally, dynamic programming picks a set of non-overlapping regions that match the model. The cell model requires few images with simple dot annotation for training and can be learned within a structured SVM framework. In the reported experiments, state-of-the-art cell detection accuracy is achieved for H&E-stained histology, fluorescence, and phase-contrast images.