Computational Pathology

Computational pathology is an approach to diagnosis that incorporates multiple sources of data (e.g., pathology, radiology, clinical, molecular and lab operations); uses mathematical models to generate diagnostic inferences; and presents clinically actionable knowledge to customers. This vision goes beyond an informatics-centric view and leverages the core competency of pathology and the ability to effectively communicate clinically actionable knowledge. Discover the latest research on computational pahtology here.

March 6, 2020
Open Access

Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning

Saima RathoreAhmad Chaddad
March 21, 2020
Open Access

Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence

NPJ Digital Medicine
Shivam KalraLiron Pantanowitz
May 26, 2020

The Use of Artificial Intelligence and Deep Machine Learning in Oncologic Histopathology

Journal of Oral Pathology & Medicine : Official Publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
Ahmed S SultanJohn R Basile
April 29, 2020

Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology

IEEE Transactions on Visualization and Computer Graphics
Alberto CorvoJarke van Wijk
February 5, 2020
Open Access

Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images

IEEE Transactions on Medical Imaging
Muhammad ShabanNasir Rajpoot
April 22, 2020

PanNuke Dataset Extension, Insights and Baselines

Jevgenij GamperNasir Rajpoot
May 12, 2020

Unsupervised Machine Learning in Pathology: The Next Frontier

Surgical Pathology Clinics
Adil RoohiPhedias Diamandis
February 6, 2020

A mean field approach to model levels of consciousness from EEG recordings

Marco Alberto JavaroneSrivas Chennu

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