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Deep Learning Approaches in Medical Image Segmentation

KDnuggets

Medical imaging has been revolutionized by the adoption of deep learning techniques. The use of this branch of machine learning has ushered in a new era of precision and efficiency in medical image segmentation, a central analytical process in modern healthcare diagnostics and treatment planning.

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Accelerating Academic Medical Research with an AI-Driven Data Strategy

Snowflake

Academic medical centers (AMCs) are a critical keystone of healthcare systems worldwide. They serve as major hubs of medical research, pioneering new treatments that advance and set the standard of care throughout medicine. The post Accelerating Academic Medical Research with an AI-Driven Data Strategy appeared first on Snowflake.

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AI-Driven Data Strategy for Academic Medical Center Research

Snowflake

Academic medical centers (AMCs) are a critical keystone of healthcare systems worldwide. They serve as major hubs of medical research, pioneering new treatments that advance and set the standard of care throughout medicine. Patients who receive care at AMCs are more likely to receive the most up-to-date therapies and treatments.

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Data Science and Analytics in Personalized Healthcare: Progress and Prospects

KDnuggets

Accordingly, personalized healthcare aims to provide patients with treatments and medical assistance that are tailored to their unique health status, medical history, and lifestyle: simply put, tailored to their. Instead, there has been a gradual shift towards more individualized and patient-centered solutions.

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Health Care Outside of the Box

Cloudera

A simple Google search will show that there are many suggestions from elderly homes, care centers, medical practices, law firms, and associations for seniors that address how to have these conversations. The patient is constantly being assessed for health issues, and immediate action can be taken when needed.

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Processing medical images at scale on the cloud

Tweag

To allow innovation in medical imaging with AI, we need efficient and affordable ways to store and process these WSIs at scale. Most training pipelines and systems are designed to handle fairly small, sub-megapixel images.

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Types of Bias in Machine Learning

KDnuggets

business, security, medical, education etc.). The sample data used for training has to be as close a representation of the real scenario as possible. There are many factors that can bias a sample from the beginning and those reasons differ from each domain (i.e.