
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. |
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Relators: | Fabrizio Lamberti, Lia Morra |
Academic year: | 2020/21 |
Publication type: | Electronic |
Number of Pages: | 84 |
Additional Information: | Tesi secretata. Fulltext non presente |
Subjects: | |
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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