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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

Abstract:

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.

Relatori: Fabrizio Lamberti, Lia Morra
Anno accademico: 2020/21
Tipo di pubblicazione: Elettronica
Numero di pagine: 84
Informazioni aggiuntive: Tesi secretata. Fulltext non presente
Soggetti:
Corso di laurea: Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering)
Classe di laurea: Nuovo ordinamento > Laurea magistrale > LM-32 - INGEGNERIA INFORMATICA
Aziende collaboratrici: NON SPECIFICATO
URI: http://webthesis.biblio.polito.it/id/eprint/18098
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