IMAGE ANALYSIS AND MACHINE LEARNING
The quantitative features found in radiology scans and pathology slides alone have the ability to uncover disease characteristics that are invisible to the naked eye. By combining structured radiomics with other orthogonal data in the Tempus database, we hope to eventually improve the accuracy of diagnosis and enhance prognosis for patients. This integrated data also supports biomarker development and drug discovery in a research setting.
Our highly-automated research image analysis is optimized around advanced pattern recognition and data characterization. The program is designed to quantify various tumor characteristics in a non-invasive and objective way.
Our complex machine-learning algorithms are constantly running in the background, which allows us to improve the speed and accuracy of our insights engine in real time.
Our advanced technologies detect and count mitotic events, segmentation of nuclei and cells, path-omics feature extraction, and tissue classification.
We transform 2D imaging slices into 3D contoured tumor models using advanced algorithms and technology paired with expert radiologists to extract key imaging characteristics for objective and quantifiable research analysis.
By combining our web-based pathology and radiology platforms with state-of-the-art image analysis tools, we enable pathologists and researchers to access, share and analyze images anywhere at anytime.
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