AI tools models are a powerful tool in cancer treatment. However, unless these algorithms are properly calibrated, they can sometimes make inaccurate or biased predictions.
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A deep learning-based technique can be used to eliminate the need for special stains to be prepared by human histotechnologists.
Using AI and mobile digital microscopy, researchers hope to create screening tools that can detect precursors to cervical cancer in women in resource-limited settings.
Pathologists who examined the computationally stained images could not tell them apart from traditionally stained slides.
Researchers at the University of Connecticut have developed a lensless microscope that allows an observer to enjoy an enormous field of view.
By adding infrared capability to the ubiquitous, standard optical microscope, researchers hope to bring cancer diagnosis into the digital era.
A novel method of combining advanced optical imaging with an artificial intelligence algorithm produces accurate, real-time intraoperative diagnosis of brain tumors.
An AI has successfully found features in pathology images from human cancer patients, without annotation, that could be understood by human doctors.
A software tool uses artificial intelligence to recognize cancer cells from digital pathology images — giving clinicians a powerful way of predicting patient outcomes.
Researchers used artificial intelligence to develop a new classification method which identifies the primary origins of cancerous tissue based on chemical DNA changes.
Researchers have utilized machine learning capabilities to assist with the challenging task of grading tumor patterns and subtypes of lung adenocarcinoma.
A team of experts led by two University of Michigan researchers calls for attention to this shadow record.