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Joint Registration and Multi-View Lesion Detection in Mammography

Luigi Manco

Joint Registration and Multi-View Lesion Detection in Mammography.

Rel. Fabrizio Lamberti, Lia Morra. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2021


Many medical imaging applications entail the acquisition of images from multiple points of view: in mammography two views are acquired in orthogonal directions which allow the radiologist, for instance, to rule out more easily false positives, which often appear only on a single view. This research thesis introduces and analyses a multi-view deep learning architecture that is trained for the task of breast cancer lesion detection on registered cranio-caudal (CC) and medio-lateral oblique (MLO) mammography views. The main innovation behind this methodology lies in the dual-stream structure of the network and a preliminary registration of the CC and MLO views that are jointly fed in the multi-view network as opposed to the usual one-at-a-time image processing of current object detectors, which is the dominant approach in the field. The registration task in mammography is particularly challenging; among the challenges are the tissue distortions between views, a consequence of the compression introduced by the exam modalities. The feasibility of this approach is established through experimental validation on the CBIS-DDSM dataset.

Relators: Fabrizio Lamberti, Lia Morra
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 84
Additional Information: Tesi secretata. Fulltext non presente
Corso di laurea: Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering)
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/18098
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