Search Results for author:
Found 38 papers, 5 papers with code
Deep learning-enabled prediction of surgical errors during cataract surgery: from simulation to real-world application
To our knowledge, this is the first work to address the tasks of learning surgical error prediction on a simulator using video data only and transferring this knowledge to real-world cataract surgery.
Automatic future remnant segmentation in liver resection planning
This study proposes a novel approach for automated liver resection planning, using segmentations of the liver, vessels, and tumors from CT scans to predict the future liver remnant (FLR), aiming to improve pre-operative planning accuracy and patient outcomes.
Deep Learning-Based Detection of Referable Diabetic Retinopathy and Macular Edema Using Ultra-Widefield Fundus Imaging
Results indicate that deep learning can significantly aid in the automated analysis of UWF images, potentially improving the efficiency and accuracy of DR and DME detection in clinical settings.
Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation
The extraction of blood vessels has recently experienced a widespread interest in medical image analysis.
Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation
Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-independent Couinaud segments.
Deep vessel segmentation with joint multi-prior encoding
The integration of shape and topological priors into vessel segmentation models has been shown to improve segmentation accuracy by offering contextual information about the shape of the blood vessels and their spatial relationships within the vascular tree.
Learning Transferability in Deep Segmentation of Liver Metastases
The ability to transfer knowledge and models across dif-ferent datasets and clinical scenarios is of paramount impor-tance in medical imaging.
A review of deep learning-based information fusion techniques for multimodal medical image classification
Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it combines information from various imaging modalities to provide a more comprehensive understanding of the underlying pathology.
LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression
This work proposes a novel framework for analyzing disease progression using time-aware neural ordinary differential equations (NODE).
Guidelines for Cerebrovascular Segmentation: Managing Imperfect Annotations in the context of Semi-Supervised Learning
Based on these experiments, we provide guidelines for the annotation and training of cerebrovascular segmentation models.
L-MAE: Longitudinal masked auto-encoder with time and severity-aware encoding for diabetic retinopathy progression prediction
Our results demonstrated the relevancy of both time-aware position embedding and masking strategies based on disease progression knowledge.
DISCOVER: 2-D Multiview Summarization of Optical Coherence Tomography Angiography for Automatic Diabetic Retinopathy Diagnosis
A straightforward solution to this task is a 3-D neural network classifier.
Automated Detection of Myopic Maculopathy in MMAC 2023: Achievements in Classification, Segmentation, and Spherical Equivalent Prediction
As for Task 3 (prediction of spherical equivalent), we have designed a deep regression model based on the data distribution of the dataset and employed an integration strategy to enhance the model's prediction accuracy.
Longitudinal Self-supervised Learning Using Neural Ordinary Differential Equation
In recent years, a novel class of algorithms has emerged with the goal of learning disease progression in a self-supervised manner, using either pairs of consecutive images or time series of images.
LMT: Longitudinal Mixing Training, a Framework to Predict Disease Progression from a Single Image
Our framework, Longitudinal Mixing Training (LMT), can be considered both as a regularizer and as a pretext task that encodes the disease progression in the latent space.
Improved Automatic Diabetic Retinopathy Severity Classification Using Deep Multimodal Fusion of UWF-CFP and OCTA Images
Diabetic Retinopathy (DR), a prevalent and severe complication of diabetes, affects millions of individuals globally, underscoring the need for accurate and timely diagnosis.
Cross-dimensional transfer learning in medical image segmentation with deep learning
In this paper, we introduce an efficient way to transfer the efficiency of a 2D classification network trained on natural images to 2D, 3D uni- and multi-modal medical image segmentation applications.
Cross-modal tumor segmentation using generative blending augmentation and self training
\textit{Objectives}: Data scarcity and domain shifts lead to biased training sets that do not accurately represent deployment conditions.
Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic Retinopathy
Diabetic Retinopathy (DR) is a severe complication of diabetes that can cause blindness.
Multimodal Information Fusion for Glaucoma and DR Classification
In recent years, multiple imaging techniques have been used in clinical practice for retinal analysis: 2D fundus photographs, 3D optical coherence tomography (OCT) and 3D OCT angiography, etc.
Detection of diabetic retinopathy using longitudinal self-supervised learning
Longitudinal imaging is able to capture both static anatomical structures and dynamic changes in disease progression towards earlier and better patient-specific pathology management.
Mapping the ocular surface from monocular videos with an application to dry eye disease grading
With a prevalence of 5 to 50%, Dry Eye Disease (DED) is one of the leading reasons for ophthalmologist consultations.
Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors
Clinical diagnosis of the pediatric musculoskeletal system relies on the analysis of medical imaging examinations.
Regularized directional representations for medical image registration
In image registration, many efforts have been devoted to the development of alternatives to the popular normalized mutual information criterion.
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
Morphological and diagnostic evaluation of pediatric musculoskeletal system is crucial in clinical practice.
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).
