Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Journal of Mathematical Imaging and Vision
Modern Computational Intelligence Methods for the Interpretation of Medical Images
Modern Computational Intelligence Methods for the Interpretation of Medical Images
Computer-aided interpretation of medical images: mammography case study
Machine Graphics & Vision International Journal
Morphological Component Analysis: An Adaptive Thresholding Strategy
IEEE Transactions on Image Processing
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This paper discusses a concept of computational understanding ofmedical images in a context of computer-aided diagnosis. Fundamental research purpose was improved diagnosis of the cases, formulated by human experts. Designed methods of soft computing with extremely important role of: a) semantically sparse data representation, b) determined specific information, formally and experimentally, and c) computational intelligence approach were adjusted to the challenges of image-based diagnosis. Formalized description of image representation procedures was completed with exemplary results of chosen applications, used to explain formulated concepts, to make them more pragmatic and assure diagnostic usefulness. Target pathology was ontologically described, characterized by as stable as possible patterns, numerically described using semantic descriptors in sparse representation. Adjusting of possible source pathology to computationalmap of target pathologywas fundamental issue of considered procedures. Computational understandingmeans: a) putting together extracted and numerically described content, b) recognition of diagnostic meaning of content objects and their common significance, and c) verification by comparative analysiswith all accessible information and knowledge sources (patient record, medical lexicons, the newest communications, reference databases, etc.).