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Corvo A, Caballero HSG, Westenberg MA, et al. Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology. IEEE Trans Vis Comput Graph. 2021;27(10):3851-3866doi: 10.1109/TVCG.2020.2990336.
Corvo, A., Caballero, H. S. G., Westenberg, M. A., van Driel, M. A., & van Wijk, J. J. (2021). Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology. IEEE transactions on visualization and computer graphics, 27(10), 3851-3866. https://doi.org/10.1109/TVCG.2020.2990336
Corvo, A, et al. "Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology." IEEE transactions on visualization and computer graphics vol. 27,10 (2021): 3851-3866. doi: https://doi.org/10.1109/TVCG.2020.2990336
Corvo A, Caballero HSG, Westenberg MA, van Driel MA, van Wijk JJ. Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology. IEEE Trans Vis Comput Graph. 2021 Oct;27(10):3851-3866. doi: 10.1109/TVCG.2020.2990336. Epub 2021 Sep 01. PMID: 32340951.
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IEEE Trans Vis Comput Graph. 2021 Oct;27(10):3851-3866. doi: 10.1109/TVCG.2020.2990336. Epub 2021 Sep 01.

Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology.

IEEE transactions on visualization and computer graphics

A Corvo, H S Garcia Caballero, M A Westenberg, M A van Driel, J J van Wijk

PMID: 32340951 DOI: 10.1109/TVCG.2020.2990336

Abstract

Recent advances in computational and algorithmic power are evolving the field of medical imaging rapidly. In cancer research, many new directions are sought to characterize patients with additional imaging features derived from radiology and pathology images. The emerging field of Computational Pathology targets the high-throughput extraction and analysis of the spatial distribution of cells from digital histopathology images. The associated morphological and architectural features allow researchers to quantify and characterize new imaging biomarkers for cancer diagnosis, prognosis, and treatment decisions. However, while the image feature space grows, exploration and analysis become more difficult and ineffective. There is a need for dedicated interfaces for interactive data manipulation and visual analysis of computational pathology and clinical data. For this purpose, we present IIComPath, a visual analytics approach that enables clinical researchers to formulate hypotheses and create computational pathology pipelines involving cohort construction, spatial analysis of image-derived features, and cohort analysis. We demonstrate our approach through use cases that investigate the prognostic value of current diagnostic features and new computational pathology biomarkers.

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