204 citations · 223 across the 9 of their papers we have counts for
9 papers
The Blind Spot in 2D Infants' Pose Estimation:Robust Learning from Noisy Annotations
Emanuele Cardinale, Marco Proietti, Alessandro Cacciatore +3
Noisy annotations pose a significant challenge for supervised deep learning, as neural networks rely on large-scale, high-quality labeled data whose corruption can severely impair…
Evidence-Based Text-Conditioned 3D CT Synthesis for Ovarian Cancer
Francesca Pia Panaccione, Eugenio Lomurno, Francesca Fati +11
Ovarian cancer is frequently diagnosed at an advanced stage, making preoperative contrast-enhanced computed tomography (CT) central to staging and surgical planning; yet the scarci…
Vision Transformers for Preoperative CT-Based Prediction of Histopathologic Chemotherapy Response Score in High-Grade Serous Ovarian Carcinoma
Francesca Fati, Felipe Coutinho, Marika Reinius +25
Purpose. High-grade serous ovarian carcinoma (HGSOC) is characterized by pronounced biological and spatial heterogeneity and is frequently diagnosed at an advanced stage. Neoadjuva…
Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research
Ciro Benito Raggio, Lucia Migliorelli, Nils Skupien +6
Federated Learning (FL) holds the potential to advance equality in health by enabling diverse institutions to collaboratively train deep learning (DL) models, even with limited dat…
Dual-energy extraction for proton therapy and imaging: validation on a clinical synchrotron-based facility
Alexander A. Pryanichnikov, Dmitry Shwartz, Jon Feldmand +2
The purpose of this study was to investigate the feasibility of extracting proton beams with two distinct energies within a single synchrotron cycle. The energy difference between…
A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck
Ciro Benito Raggio, Paolo Zaffino, Maria Francesca Spadea
Shortened Abstract Cone-beam computed tomography (CBCT) has become a widely adopted modality for image-guided radiotherapy (IGRT). However, CBCT suffers from increased noise, limit…
FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis
Ciro Benito Raggio, Mathias Krohmer Zabaleta, Nils Skupien +6
The generation of Synthetic Computed Tomography (sCT) images has become a pivotal methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) treatmen…
LIMIS: Towards Language-based Interactive Medical Image Segmentation
Lena Heinemann, Alexander Jaus, Zdravko Marinov +4
Within this work, we introduce LIMIS: The first purely language-based interactive medical image segmentation model. We achieve this by adapting Grounded SAM to the medical domain a…
Deep learning-based synthetic-CT generation in radiotherapy and PET: a review
Maria Francesca Spadea, Matteo Maspero, Paolo Zaffino +1
Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical one…