Search Results for author:
Found 17 papers, 7 papers with code
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.
Multi-Head Multi-Loss Model Calibration
Delivering meaningful uncertainty estimates is essential for a successful deployment of machine learning models in the clinical practice.
Test Time Transform Prediction for Open Set Histopathological Image Recognition
In test time, we measure model confidence in predicting this transform, which we expect to be lower for images in the Open Set.
A Hierarchical Multi-Task Approach to Gastrointestinal Image Analysis
We also describe in this paper our solution for the challenge task of polyp segmentation in colonoscopies, which was addressed with a pretrained double encoder-decoder network.
Convolutional Nets Versus Vision Transformers for Diabetic Foot Ulcer Classification
This paper compares well-established Convolutional Neural Networks (CNNs) to recently introduced Vision Transformers for the task of Diabetic Foot Ulcer Classification, in the context of the DFUC 2021 Grand-Challenge, in which this work attained the first position.
Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation
Polyps represent an early sign of the development of Colorectal Cancer.
Balanced-MixUp for Highly Imbalanced Medical Image Classification
The resulting two sets of samples are then mixed-up to create a more balanced training distribution from which a neural network can effectively learn without incurring in heavily under-fitting the minority classes.
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.
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.
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.
Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI
Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences.
3D convolutional neural network for abdominal aortic aneurysm segmentation
An abdominal aortic aneurysm (AAA) is a focal dilation of the aorta that, if not treated, tends to grow and may rupture.
Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using deep convolutional neural networks
We propose a new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducible thrombus region of interest detection and subsequent fine thrombus segmentation.
