Search Results for author:
Found 14 papers, 4 papers with code
Automatic facial axes standardization of 3D fetal ultrasound images
In conclusion, the proposed network demonstrates potential for improving the consistency and accuracy of fetal facial assessments in clinical settings, facilitating early evaluation of craniofacial anomalies.
Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration
Accurate segmentation of fetal brain magnetic resonance images is crucial for analyzing fetal brain development and detecting potential neurodevelopmental abnormalities.
BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs
We present a 3D face reconstruction system that aims at recovering the 3D facial geometry of babies from uncalibrated photographs, BabyNet.
Memory-aware curriculum federated learning for breast cancer classification
Our curriculum controls the order of the training samples paying special attention to those that are forgotten after the deployment of the global model.
An Uncertainty-aware Hierarchical Probabilistic Network for Early Prediction, Quantification and Segmentation of Pulmonary Tumour Growth
Early detection and quantification of tumour growth would help clinicians to prescribe more accurate treatments and provide better surgical planning.
Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans
We address the problem of supporting radiologists in the longitudinal management of lung cancer.
Survey on 3D face reconstruction from uncalibrated images
Recently, a lot of attention has been focused on the incorporation of 3D data into face analysis and its applications.
Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertainty
In this paper, we propose a method for the automatic classification of proximal femur fractures into 3 and 7 AO classes based on a Convolutional Neural Network (CNN).
Pulmonary Nodule Malignancy Classification Using its Temporal Evolution with Two-Stream 3D Convolutional Neural Networks
Nodule malignancy assessment is a complex, time-consuming and error-prone task.
Medical-based Deep Curriculum Learning for Improved Fracture Classification
Current deep-learning based methods do not easily integrate to clinical protocols, neither take full advantage of medical knowledge.
Re-Identification and Growth Detection of Pulmonary Nodules without Image Registration Using 3D Siamese Neural Networks
Lung cancer follow-up is a complex, error prone, and time consuming task for clinical radiologists.
Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline
The early identification of malignant pulmonary nodules is critical for better lung cancer prognosis and less invasive chemo or radio therapies.
Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation
In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
