Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Optimal Mass Transport for Registration and Warping
International Journal of Computer Vision
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Automated sub-cellular phenotype classification: an introduction and recent results
WISB '06 Proceedings of the 2006 workshop on Intelligent systems for bioinformatics - Volume 73
Instance-based generative biological shape modeling
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
All of Statistics: A Concise Course in Statistical Inference
All of Statistics: A Concise Course in Statistical Inference
Penalized Fisher discriminant analysis and its application to image-based morphometry
Pattern Recognition Letters
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We propose a novel method for detecting characteristic informative phenotype patterns from biomedical images. By building a metric space quantifying the difference between images, we learn the distributions of different classes, and then detect the characteristic regions using graph partition. We show that the detected regions are statistically significant. Our approach can also be used for designing differentiating features for specific data set. We apply our method to a digital pathology problem and successfully detect two characteristic phenotypes pertaining to normal liver and hepatoblastoma nuclei. In addition to digital pathology, our method can be applied to other biomedical problems for detecting characteristic phenotypes (e.g. location proteomics, genetic screens, cell mechanics, etc.).