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).
|Titolo:||Compressing medical images with minimal information loss|
|Data di pubblicazione:||20-dic-2019|
|Aree SISSA:||Laboratorio Interdisciplinare|
|Appare nelle tipologie:||8.4 Master thesis in High Performance Computing (HPC)|