DeepLook: a deep learning computed diagnosis support for breast tomosynthesis
Giovanni Mettivier, Roberta Ricciarci, Antonio Sarno, Francesca Saveria Maddaloni, M. Porzio, Mariacarla Staffa, S. Minelli, Angela Santoro, Elena Antignani, Marica Masi, Valeria Landoni, Pedro Ordòñez, Francesca Ferranti, Laura Greco, Stefania Clemente, Paolo Russo · 2022
The aim of the DeepLook project, funded by INFN (Italy), is to implement a deep learning architecture for Computed Aided Detection (CAD), based on neural networks developed with deep learning methods, for the automatic detection and classification of breast lesions in DBT images. A preliminary step (started 2 years ago and still ongoing) was the creation of a dataset of annotated images. This dataset includes images acquired with different clinical DBT units and different acquisition geometries, on several hundred patients, containing a variety of possible breast lesions and normal cases of absence of lesions. This will make the diagnostic capacity of the CAD system particularly extensive in various clinical situations and on a significant sample of patients, so allowing the network to diagnose various types of lesions (at the level of the single tomosynthesis slices) and capable of operate on commercial DBT systems, also available from different vendors, as found in breast diagnosis departments. The developed CAD and first result of the indication of the slice containing the suspected mass will be presented.