Search Results for author:
Found 14 papers, 1 papers with code
Multi-Task, Multi-Domain Deep Segmentation with Shared Representations and Contrastive Regularization for Sparse Pediatric Datasets
Automatic segmentation of magnetic resonance (MR) images is crucial for morphological evaluation of the pediatric musculoskeletal system in clinical practice.
Multi-Structure Deep Segmentation with Shape Priors and Latent Adversarial Regularization
Automatic segmentation of the musculoskeletal system in pediatric magnetic resonance (MR) images is a challenging but crucial task for morphological evaluation in clinical practice.
Efficient embedding network for 3D brain tumor segmentation
As the input data is in 3D, the first layers of the encoder are devoted to the reduction of the third dimension in order to fit the input of the EfficientNet network.
Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network
To overcome the scarcity and heterogeneity of pediatric datasets, we adopt a regularization strategy to improve the generalization of segmentation models.
ExplAIn: Explanatory Artificial Intelligence for Diabetic Retinopathy Diagnosis
In recent years, Artificial Intelligence (AI) has proven its relevance for medical decision support.
Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention
In this work, we present a two-stage multi-scale pipeline that provides accurate mass contours from high-resolution full mammograms.
Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks
In this context, we address fully-automated multi-organ segmentation from abdominal CT and MR images using deep learning.
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0. 98 $\pm$ 0. 00 / 0. 95 $\pm$ 0. 01) but the best MSSD performance remain limited (21. 89 $\pm$ 13. 94 / 20. 85 $\pm$ 10. 63 mm).
Combining Shape Priors with Conditional Adversarial Networks for Improved Scapula Segmentation in MR images
This paper proposes an automatic method for scapula bone segmentation from Magnetic Resonance (MR) images using deep learning.
Automatic detection of rare pathologies in fundus photographs using few-shot learning
This paper presents a new few-shot learning framework that extends convolutional neural networks (CNNs), trained for frequent conditions, with an unsupervised probabilistic model for rare condition detection.
Unsupervised learning-based long-term superpixel tracking
Finding correspondences between structural entities decomposing images is of high interest for computer vision applications.
Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders
Methodological aspects are evaluated in a leave-one-out fashion on a dataset of 24 shoulder examinations from patients with obstetrical brachial plexus palsy and focus on 4 different muscles including deltoid as well as infraspinatus, supraspinatus and subscapularis from the rotator cuff.
Adaptive strategy for superpixel-based region-growing image segmentation
From an initial contour-constrained over-segmentation of the input image, the image segmentation is achieved by iteratively merging similar superpixels into regions.
Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks
Tool usage is monitored in videos recorded either through a microscope (cataract surgery) or an endoscope (cholecystectomy).
