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.
20-dic-2019
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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