199 citations · 380 across the 16 of their papers we have counts for
18 papers
Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis
Matteo Testi, Luca Clissa, Matteo Ballabio +5
Machine Learning (ML) models offer significant potential for advancing cell counting applications in neuroscience, medical research, pharmaceutical development, and environmental m…
A Federated Learning Framework for Stenosis Detection
Mariachiara Di Cosmo, Giovanna Migliorelli, Matteo Francioni +6
This study explores the use of Federated Learning (FL) for stenosis detection in coronary angiography images (CA). Two heterogeneous datasets from two institutions were considered:…
A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis
Lucia Migliorelli, Daniele Berardini, Kevin Cela +4
Background and objectives: Patients suffering from neurological diseases may develop dysarthria, a motor speech disorder affecting the execution of speech. Close and quantitative m…
Deep learning-based approaches for human motion decoding in smart walkers for rehabilitation
Carolina Gonçalves, João M. Lopes, Sara Moccia +3
Gait disabilities are among the most frequent worldwide. Their treatment relies on rehabilitation therapies, in which smart walkers are being introduced to empower the user's recov…
Learning-Based Keypoint Registration for Fetoscopic Mosaicking
Alessandro Casella, Sophia Bano, Francisco Vasconcelos +7
In Twin-to-Twin Transfusion Syndrome (TTTS), abnormal vascular anastomoses in the monochorionic placenta can produce uneven blood flow between the two fetuses. In the current pract…
Autonomous Intraluminal Navigation of a Soft Robot using Deep-Learning-based Visual Servoing
Jorge F. Lazo, Chun-Feng Lai, Sara Moccia +7
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 endos…
Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings
Sophia Bano, Alessandro Casella, Francisco Vasconcelos +28
Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomos…
A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis
Maria Chiara Fiorentino, Francesca Pia Villani, Mariachiara Di Cosmo +2
Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. Despite a large number of survey papers already present in this field, most of…
Real-Time Human Pose Estimation on a Smart Walker using Convolutional Neural Networks
Manuel Palermo, Sara Moccia, Lucia Migliorelli +2
Rehabilitation is important to improve quality of life for mobility-impaired patients. Smart walkers are a commonly used solution that should embed automatic and objective tools fo…
How can we learn (more) from challenges? A statistical approach to driving future algorithm development
Tobias Roß, Pierangela Bruno, Annika Reinke +12
Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step fo…
FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset
Sophia Bano, Alessandro Casella, Francisco Vasconcelos +9
Fetoscopy laser photocoagulation is a widely used procedure for the treatment of Twin-to-Twin Transfusion Syndrome (TTTS), that occur in mono-chorionic multiple pregnancies due to…
A transfer-learning approach for lesion detection in endoscopic images from the urinary tract
Jorge F. Lazo, Sara Moccia, Aldo Marzullo +5
Ureteroscopy and cystoscopy are the gold standard methods to identify and treat tumors along the urinary tract. It has been reported that during a normal procedure a rate of 10-20…
Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy
Jorge F. Lazo, Aldo Marzullo, Sara Moccia +4
Purpose: Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma (UTUC). During ureteroscopy, the a…
A Lumen Segmentation Method in Ureteroscopy Images based on a Deep Residual U-Net architecture
Jorge F. Lazo, Aldo Marzullo, Sara Moccia +4
Ureteroscopy is becoming the first surgical treatment option for the majority of urinary affections. This procedure is performed using an endoscope which provides the surgeon with…
Comparison of different CNNs for breast tumor classification from ultrasound images
Jorge F. Lazo, Sara Moccia, Emanuele Frontoni +1
Breast cancer is one of the deadliest cancer worldwide. Timely detection could reduce mortality rates. In the clinical routine, classifying benign and malignant tumors from ultraso…
Preterm infants' pose estimation with spatio-temporal features
Sara Moccia, Lucia Migliorelli, Virgilio Carnielli +1
Objective: Preterm infants' limb monitoring in neonatal intensive care units (NICUs) is of primary importance for assessing infants' health status and motor/cognitive development.…
Preterm infants' limb-pose estimation from depth images using convolutional neural networks
Sara Moccia, Lucia Migliorelli, Rocco Pietrini +1
Preterm infants' limb-pose estimation is a crucial but challenging task, which may improve patients' care and facilitate clinicians in infant's movements monitoring. Work in the li…
Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy
S. Moccia, S. J. Wirkert, H. Kenngott +6
Objective: Surgical data science is evolving into a research field that aims to observe everything occurring within and around the treatment process to provide situation-aware data…