Automated asymmetry detection in digital mammography

Population-based breast cancer screening programs with mammography have proven to reduce mortality and morbidity associated with advanced stage of disease. Screening mammography included two views of each breast (craniocaudal-CC and mediolateral oblique-MLO).
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$ 1.6T
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the problem
Nature and Context

One of the suspicious mammographic findings for mass is asymmetries, findings that represent unilateral deposition of fibroglandulair tissue not confirming completely to definition of a mass. Asymmetries are classified in four groups: 1. Asymmetry : as an area of fibroglandulair tissue visible on only one mammographic projection, mostly caused by superimposition of normal breast tissue.2. Focal Asymmetry: visible on two projections, hence a real find in rather than superposition.3. Global Asymmetry: consisting of an asymmetry over at least one-quarter of breast and is usually a normal variant.4. Developing Asymmetry: new, larger and more conspicuous than on a previous examination. Among these four types we want to work on type 2(Focal Asymmetry) because this type has to be differentiated from the mass. For this reason radiologists request additional view, Focal Compression Magnification view (FCMV), from the desired Focal Asymmetry to see if there is a real mass under it(asymmetry does not resolve in FCMV) or not . Each additional view has radiation dose equal to one chest X-ray. We aim to produce AI algorithm to detect Focal Asymmetries, with accuracy similar to an expert radiologist, which can predict the probability of being resolved in FCMV (low/high probability).

Ideas Description

Inland Imaging, the largest radiology company on the west coast, is a pioneer in new imaging techniques. They led the charge in standardizing 3D-mammography.

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