We address the problem of analyzing histopathology images, using computational tools, to automatically find patterns related with pathology signatures associated with healthy and abnormal tissues, which are 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.


Victor Hugo Contreras Ordoñez

Juan Sebastián Lara Ramírez

Johan David Rodriguez  Portela


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