Search Results for author: Sara Moccia

Found 16 papers, 2 papers with code

A Federated Learning Framework for Stenosis Detection

no code implementations30 Oct 2023 Mariachiara Di Cosmo, Giovanna Migliorelli, Matteo Francioni, Andi Mucaj, Alessandro Maolo, Alessandro Aprile, Emanuele Frontoni, Maria Chiara Fiorentino, Sara Moccia

Our results showed that the FL framework does not substantially affects clients 2 performance, which already achieved good performance with local training; for client 1, instead, FL framework increases the performance with respect to local model of +3. 76%, +17. 21% and +10. 80%, respectively, reaching P rec = 73. 56, Rec = 67. 01 and F1 = 70. 13.

Federated Learning

A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis

no code implementations16 Sep 2023 Lucia Migliorelli, Daniele Berardini, Kevin Cela, Michela Coccia, Laura Villani, Emanuele Frontoni, Sara Moccia

This architecture, called facial landmark Mask RCNN, aims at locating facial landmarks as a prior for assessing the orofacial functions related to speech and examining dysarthria evolution in neurological diseases.

Deep learning-based approaches for human motion decoding in smart walkers for rehabilitation

no code implementations13 Jan 2023 Carolina Gonçalves, João M. Lopes, Sara Moccia, Daniele Berardini, Lucia Migliorelli, Cristina P. Santos

Promising results were attained for early action detection as a human motion decoding strategy, with enhancements in the focus of the proposed architectures.

Action Detection Action Recognition

Autonomous Intraluminal Navigation of a Soft Robot using Deep-Learning-based Visual Servoing

no code implementations1 Jul 2022 Jorge F. Lazo, Chun-Feng Lai, Sara Moccia, Benoit Rosa, Michele Catellani, Michel de Mathelin, Giancarlo Ferrigno, Paul Breedveld, Jenny Dankelman, Elena De Momi

Navigation inside luminal organs is an arduous task that requires non-intuitive coordination between the movement of the operator's hand and the information obtained from the endoscopic video.

Autonomous Navigation Decision Making +1

A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis

no code implementations28 Jan 2022 Maria Chiara Fiorentino, Francesca Pia Villani, Mariachiara Di Cosmo, Emanuele Frontoni, Sara Moccia

This paper ends with a critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working in the field to translate the research methodology into the actual clinical practice.

FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset

1 code implementation10 Jun 2021 Sophia Bano, Alessandro Casella, Francisco Vasconcelos, Sara Moccia, George Attilakos, Ruwan Wimalasundera, Anna L. David, Dario Paladini, Jan Deprest, Elena De Momi, Leonardo S. Mattos, Danail Stoyanov

Through the \textit{Fetoscopic Placental Vessel Segmentation and Registration (FetReg)} challenge, we present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos.

Segmentation Semantic Segmentation

A transfer-learning approach for lesion detection in endoscopic images from the urinary tract

no code implementations8 Apr 2021 Jorge F. Lazo, Sara Moccia, Aldo Marzullo, Michele Catellani, Ottavio De Cobelli, Benoit Rosa, Michel de Mathelin, Elena De Momi

In this work we study the implementation of 3 different Convolutional Neural Networks (CNNs), using a 2-steps training strategy, to classify images from the urinary tract with and without lesions.

Lesion Detection Transfer Learning

Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

no code implementations5 Apr 2021 Jorge F. Lazo, Aldo Marzullo, Sara Moccia, Michele Catellani, Benoit Rosa, Michel de Mathelin, Elena De Momi

Of these, two architectures are taken as core-models, namely U-Net based in residual blocks($m_1$) and Mask-RCNN($m_2$), which are fed with single still-frames $I(t)$.


Preterm infants' pose estimation with spatio-temporal features

no code implementations8 May 2020 Sara Moccia, Lucia Migliorelli, Virgilio Carnielli, Emanuele Frontoni

Assessment of the proposed framework is performed through a comprehensive study with sixteen depth videos acquired in the actual clinical practice from sixteen preterm infants (the babyPose dataset).

Pose Estimation

Preterm infants' limb-pose estimation from depth images using convolutional neural networks

no code implementations26 Jul 2019 Sara Moccia, Lucia Migliorelli, Rocco Pietrini, Emanuele Frontoni

Preterm infants' limb-pose estimation is a crucial but challenging task, which may improve patients' care and facilitate clinicians in infant's movements monitoring.

Pose Estimation

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