This thesis aims to explore the potentialities of neural networks as compression algorithms for medical images. The objective is to develop a compressed image representation suitable for image comparison. In particular we studied different autoencoder architectures, varying the encoding mechanism in order to achieve a high degree of compression while also retaining a meaningful feature space. Our work is focused on mammograms but the methods introduced here can be extrapolated to other types of medical images.

Compressing medical images with minimal information loss / Barone, Federico. - (2019 Dec 20).

Compressing medical images with minimal information loss

Barone, Federico
2019-12-20

Abstract

This thesis aims to explore the potentialities of neural networks as compression algorithms for medical images. The objective is to develop a compressed image representation suitable for image comparison. In particular we studied different autoencoder architectures, varying the encoding mechanism in order to achieve a high degree of compression while also retaining a meaningful feature space. Our work is focused on mammograms but the methods introduced here can be extrapolated to other types of medical images.
Non assegn
Laio, Alessandro
Heltai, Luca
SARTORI, Alberto
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Descrizione: MHPC Thesis
Tipologia: Tesi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/115945
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