Screenshot 2018-08-06 22.48.51

Visual Analytics in histopathology diagnostics: a protocol-based approach

Supporting the pathologist with Computer-Aided Quantitative Analysis 

Computer-Aided-Diagnosis (CAD) systems supporting the diagnostic process are widespread in radiology. Digital Pathology is still behind in the introduction of such solutions. Several studies investigated pathologists’ behavior but only a few aimed to improve the diagnostic and report process with novel applications. In this work we designed and implemented a first protocol-based CAD viewer supported by visual analytics. The system targets the optimization of the diagnostic workflow in breast cancer diagnosis by means of three image analysis features that belong to the standard grading system (Nottingham Histologic Grade). A pathologist’s routine was tracked during the examination of breast cancer tissue slides and diagnostic traces were analyzed from a qualitative perspective. Accordingly, a set of generic requirements was elicited to define the design and the implementation of the CAD-Viewer. A first qualitative evaluation conducted with five pathologists shows that the interface suffices the diagnostic workflow and diminishes the manual effort. We present promising evidence of the usefulness of our CAD-viewer and opportunities for its extension and integration in clinical practice. As a conclusion, the findings demonstrate that it is feasibile to optimize the Nottingham Grading workflow and, generally, the histological diagnosis by integrating computational pathology data with visual analytics techniques.

A short demo that shows how pathologists can take advantage of CAD-Tiles in tumor cell examination and mitotic counting

Enabling Interactive Process Analysis with Process Mining and Visual Analytics

Authors: Prabhakar Dixit, H.S.G.Caballero, Alberto Corvò, B.Hompes, J.C.A. Buijs, W.M.P.van der Aalst


In a typical healthcare setting, specific clinical care pathways can be defined by the hospitals. Process mining provides a way of analyzing the care pathways by analyzing the event data extracted from the hospital information systems. Process mining can be used to optimize the overall care pathway, and gain interesting insights into the actual execution of the process, as well as to compare the expectations versus the reality. In this paper, a generic novel tool called InterPretA, is introduced which builds upon pre-existing process mining and visual analytics techniques to enable the user to perform such process oriented analysis. InterPretA contains a set of options to provide high level conformance analysis of a process from different perspectives. Furthermore, InterPretA enables detailed investigative analysis by letting the user interactively analyze, visualize and explore the execution of the processes from the data perspective.
Enabling Interactive Process Analysis with Process Mining and Visual Analytics. Available from: [accessed Sep 5, 2017].


Visual Analytics for Evaluating Clinical Pathways

Authors: H.Garcia Caballero, A.Corvò, P.Dixit, M.A. Westenberg


Digital platforms in healthcare institutions enable tracking and recording of patient care   pathways. Besides the Electronic Health Records (EHRs), the event logs from Hospital Information Systems (HIS) are a very efficient source of information, from both operational and clinical point of view. Process mining allows comparison of a patient care pathway with the event log(s) from HIS, to understand how well the reality as depicted in the event log, fits the
expectation as modeled using a care pathway. SepVis is a tool which exploits the process mining results to provide insightful and efficient visualization of an event log comprising of (suspected) sepsis patients. In this paper, we present SepVis, a visual analytics tool which aims to fill the gap in current process-centric application by looking at patients’ pathways from a clinical point of view.


PATHONE: From one thousand patients to one cell


Alberto Corvò, Michel A. Westenberg, Marc A. van Driel, Jarke J.van Wijk


Digital Pathology is a recent clinical environment in which Electronic Health Records (EHRs), biopsy data and whole-slide images (WSI) come together to provide pathologists the necessary information for making a diagnosis. Integration of this heterogeneous data into a single application is still one of the challenges in the evolution of pathology to a digital practice. While pathologists can perform diagnoses routinely on digital slides only, this is not the case in clinical research. For such purposes, the link between clinicopathological information of patients and images is essential. For example, image analysis researchers who develop automated diagnostic (support) algorithms need to select a representative set of slides to evaluate their methods. To achieve this, they need applications that combine cohort specification, slide image exploration, and selection of suitable images. We present the visualization tool PATHONE, which enables users to perform these steps on a single screen, integrating cohort and WSI selection.


The binary file is available at (New version available soon):

Right now it supports only xml files from TCGA. It is tailored to the Breast Cancer dataset. Slides have to be placed in the same directory of the binary file.