87 citations · 192 across the 13 of their papers we have counts for
4 papers · 1 filter
Ensemble Methods for Multi-Organ Segmentation in CT Series
Leonardo Crespi, Paolo Roncaglioni, Damiano Dei +5
In the medical images field, semantic segmentation is one of the most important, yet difficult and time-consuming tasks to be performed by physicians. Thanks to the recent advancem…
Comparing Adversarial and Supervised Learning for Organs at Risk Segmentation in CT images
Leonardo Crespi, Mattia Portanti, Daniele Loiacono
Organ at Risk (OAR) segmentation from CT scans is a key component of the radiotherapy treatment workflow. In recent years, deep learning techniques have shown remarkable potential…
Chest X-Rays Image Classification from beta-Variational Autoencoders Latent Features
Leonardo Crespi, Daniele Loiacono, Arturo Chiti
Chest X-Ray (CXR) is one of the most common diagnostic techniques used in everyday clinical practice all around the world. We hereby present a work which intends to investigate and…
Brain MRI Tumor Segmentation with Adversarial Networks
Edoardo Giacomello, Daniele Loiacono, Luca Mainardi
Deep Learning is a promising approach to either automate or simplify several tasks in the healthcare domain. In this work, we introduce SegAN-CAT, an approach to brain tumor segmen…