Séminaire Donnees et APprentissage Artificiel
Computer-Aided Breast Tumor Diagnosis in DCE-MRI Images
Speaker(s) : Baishali Chaudhury (University of South Florida)
The overall goal of our project is to quantify tumor heterogeneity with advanced image analysis to provide useful information about tumor biology and provide unique and valuable insight into patient treatment strategies and prognosis.We introduced a CAD (computer aided diagnosis) system to characterize breast cancer heterogeneity through spatially-explicit maps using DCE-MRI images. Through quantitative image analysis, we examined the presence of differing tumor habitats defined by initial and delayed contrast patterns within the tumor. The heterogeneity within each habitat was quantified through textural kinetic features at different scales and quantization levels. The functionality of this CAD system was then evaluated by applying it in a multi-objective framework. Various common problems in breast DCE-MRI analysis (like extremely small dataset compared to the number of extracted texture features and highly imbalanced dataset) and different data mining techniques applied in our project to deal with them will be discussed.
Fourth year PhD Candidate in University of South Florida, Tampa, USA. Currently, working on the “Analysis of DCE-MRI breast tumor images for stratifying patient prognosis”. Broader research interests include: computer vision, data mining and machine learning, sparse data representation.
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