Multimodal image retrieval to support medical case-based scientific literature search
Our long term and foremost goal is to build a system for medical case-based information retrieval on large collections of scientific biomedical literature. This goal encompasses two major research problems: (1) devising an effective multimodal representation strategy that captures the rich visual and textual content of medical cases and scientific papers; (2) designing an implementing efficient algorithms and processing strategies that can cope with the ever growing collections of scientific papers and biomedical data. This includes addressing the scalability of both the devised strategies and algorithms and the underlying technological substrate supporting them. More...
address the problem of analyzing histopathology images, using
computational tools, to automatically find patterns related with
pathology signatures associated to healthy and abnormal tissues, which
are a fundamental support for cancer diagnosis. Computational pathology
is a relatively recent research area devoted to providing accurate and
efficient computational methods to support quantitative detection,
diagnosis, and prognosis in pathology. We present several computational
and machine learning methods for efficient and effective automatic
histopathology image analysis exploiting histopathology image databases
for different digital pathology tasks including tumor and tissue
detection, location and quantification in several cancer types.