Artificial intelligence in Cancer imaging and diagnosis
The increasing incidence of cancer and the need for personalized cancer care has created a pressing need for accurate diagnosis and biomarker evaluation. Artificial Intelligence, machine learning, and deep learning have led to rapid computational pathology and precision medicine advancements. Pathologists are increasingly adopting and leveraging these technologies to deliver the right diagnosis, prognosis, and treatment to cancer patients.
Next generation diagnostic pathology - Leveraging AI to Reshape the practice of Pathology
Pathology is undergoing a transformation with an increase in workloads and the need for efficient workflows to improve the quality of diagnosis. Computer-assisted diagnosis and automated image analysis are becoming key to workflow standardization. Whole Slide Images are an accurate representation of scanned glass slides, at a high resolution and are diagnostic quality images, that aid in creating diagnostic algorithms or applications that can augment the diagnostic workflow.
Explainable AI for digital image analysis
Improving digital image analysis is a major reason AI is being incorporated into digital pathology.Coming Soon! Subscribe Now!
SCOPE H&E AI
Quantify the nuclei in H&E
Automatic cell spotting and efficient detection and quantification of nuclei is important for cancer analysis, diagnosis, classification, and grading.Coming Soon! Subscribe Now!
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WHOLE SLIDE IMAGING
Explainable AI for whole slide Image analysis
The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Our data scientists are transforming computational pathology by harnessing the power of explainable AI to empower pathologists for making accurate and faster decisions by building a diagnostic assistant. This shift from black-box AI (deep learning) to explainable AI solves the concerns related to trust, confidence, and transparency with our end users to adopt AI in pathology workflows.
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