IEEE Transactions on Pattern Analysis and Machine Intelligence
A method for linking computed image features to histological semantics in neuropathology
Journal of Biomedical Informatics
Semantic content analysis and annotation of histological images
Computers in Biology and Medicine
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Consensus of ambiguity: theory and application of active learning for biomedical image analysis
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Histological Image Feature Mining Reveals Emergent Diagnostic Properties for Renal Cancer
BIBM '11 Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine
Histological image retrieval based on semantic content analysis
IEEE Transactions on Information Technology in Biomedicine
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We propose a framework for studying visual morphological patterns across histopathological whole-slide images (WSIs). Image representation is an important component of computer-aided decision support systems for histopathological cancer diagnosis. Such systems extract hundreds of quantitative image features from digitized tissue biopsy slides and produce models for prediction. The performance of these models depends on the identification of informative features for selection of appropriate regions-of-interest (ROIs) from heterogeneous WSIs and for development of models. However, identification of informative features is hindered by the semantic gap between human interpretation of visual morphological patterns and quantitative image features. We address this challenge by using data mining and information visualization tools to study spatial patterns formed by features extracted from sub-sections of WSIs. Using ovarian serous cystadenocarcinoma (OvCa) WSIs provided by the cancer genome atlas (TCGA), we show that (1) individual and (2) multivariate image features correspond to biologically relevant ROIs, and (3) supervised image feature selection can map histopathology domain knowledge to quantitative image features.