Self-teaching AI helpful in diagnosing rare diseases
Written By : Isra Zaman
Medically Reviewed By : Dr. Kamal Kant Kohli
Published On 2022-10-11 03:45 GMT | Update On 2022-10-11 03:45 GMT
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Investigators from the Mahmood Lab at Brigham and Women's Hospital have developed a deep learning algorithm that can teach itself to learn features which can then be used to find similar cases in large pathology image repositories.
Known as SISH (Self-Supervised Image search for Histology), the new tool acts like a search engine for pathology images and has many potential applications, including identifying rare diseases and helping clinicians determine which patients are likely to respond to similar therapies.
Modern electronic databases can store an immense amount of digital records and reference images, particularly in pathology through whole slide images (WSIs). However scalability remains a pertinent roadblock for efficient use. To solve this issue, researchers developed SISH, which teaches itself to learn feature representations which can be used to find cases with analogous features in pathology at a constant speed regardless of the size of the database.
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