Search Results for author: Pierre-Henri Conze

Found 38 papers, 5 papers with code

Deep learning-enabled prediction of surgical errors during cataract surgery: from simulation to real-world application

no code implementations28 Mar 2025 Maxime Faure, Pierre-Henri Conze, Béatrice Cochener, Anas-Alexis Benyoussef, Mathieu Lamard, Gwenolé Quellec

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.

Prediction Unsupervised Domain Adaptation

Automatic future remnant segmentation in liver resection planning

no code implementations International Journal of Computer Assisted Radiology and Surgery 2025 Hicham Messaoudi, Marwan Abbas, Bogdan Badic, Douraied Ben Salem, Ahror Belaid, Pierre-Henri Conze

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

no code implementations19 Sep 2024 Philippe Zhang, Pierre-Henri Conze, Mathieu Lamard, Gwenolé Quellec, Mostafa El Habib Daho

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.

Deep Learning Image Quality Assessment

Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation

no code implementations18 Sep 2024 Amine Sadikine, Bogdan Badic, Jean-Pierre Tasu, Vincent Noblet, Pascal Ballet, Dimitris Visvikis, Pierre-Henri Conze

Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-independent Couinaud segments.

Contrastive Learning Multi-Task Learning +1

Deep vessel segmentation with joint multi-prior encoding

no code implementations18 Sep 2024 Amine Sadikine, Bogdan Badic, Enzo Ferrante, Vincent Noblet, Pascal Ballet, Dimitris Visvikis, Pierre-Henri Conze

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.

Segmentation

A review of deep learning-based information fusion techniques for multimodal medical image classification

no code implementations23 Apr 2024 Yihao Li, Mostafa El Habib Daho, Pierre-Henri Conze, Rachid Zeghlache, Hugo Le Boité, Ramin Tadayoni, Béatrice Cochener, Mathieu Lamard, Gwenolé Quellec

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.

image-classification Image Classification +2

Automated Detection of Myopic Maculopathy in MMAC 2023: Achievements in Classification, Segmentation, and Spherical Equivalent Prediction

1 code implementation8 Jan 2024 Yihao Li, Philippe Zhang, Yubo Tan, Jing Zhang, Zhihan Wang, Weili Jiang, Pierre-Henri Conze, Mathieu Lamard, Gwenolé Quellec, Mostafa El Habib Daho

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.

Classification Contrastive Learning +3

Longitudinal Self-supervised Learning Using Neural Ordinary Differential Equation

no code implementations16 Oct 2023 Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho, Yihao Li, Hugo Le Boité, Ramin Tadayoni, Pascal Massin, Béatrice Cochener, Ikram Brahim, Gwenolé Quellec, Mathieu Lamard

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.

Self-Supervised Learning

LMT: Longitudinal Mixing Training, a Framework to Predict Disease Progression from a Single Image

no code implementations16 Oct 2023 Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho, Yihao Li, Hugo Le boite, Ramin Tadayoni, Pascal Massin, Béatrice Cochener, Ikram Brahim, Gwenolé Quellec, Mathieu Lamard

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.

Cross-dimensional transfer learning in medical image segmentation with deep learning

1 code implementation29 Jul 2023 Hicham Messaoudi, Ahror Belaid, Douraied Ben Salem, Pierre-Henri Conze

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.

image-classification Image Classification +6

Multimodal Information Fusion for Glaucoma and DR Classification

no code implementations2 Sep 2022 Yihao Li, Mostafa El Habib Daho, Pierre-Henri Conze, Hassan Al Hajj, Sophie Bonnin, Hugang Ren, Niranchana Manivannan, Stephanie Magazzeni, Ramin Tadayoni, Béatrice Cochener, Mathieu Lamard, Gwenolé Quellec

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.

Classification

Detection of diabetic retinopathy using longitudinal self-supervised learning

no code implementations2 Sep 2022 Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho, Ramin Tadayoni, Pascal Massin, Béatrice Cochener, Gwenolé Quellec, Mathieu Lamard

Longitudinal imaging is able to capture both static anatomical structures and dynamic changes in disease progression towards earlier and better patient-specific pathology management.

Management Self-Supervised Learning

Regularized directional representations for medical image registration

no code implementations30 Nov 2021 Vincent Jaouen, Pierre-Henri Conze, Guillaume Dardenne, Julien Bert, Dimitris Visvikis

In image registration, many efforts have been devoted to the development of alternatives to the popular normalized mutual information criterion.

Diversity Image Registration +1

Multi-Task, Multi-Domain Deep Segmentation with Shared Representations and Contrastive Regularization for Sparse Pediatric Datasets

no code implementations21 May 2021 Arnaud Boutillon, Pierre-Henri Conze, Christelle Pons, Valérie Burdin, Bhushan Borotikar

Automatic segmentation of magnetic resonance (MR) images is crucial for morphological evaluation of the pediatric musculoskeletal system in clinical practice.

Anatomy Segmentation

Multi-Structure Deep Segmentation with Shape Priors and Latent Adversarial Regularization

no code implementations25 Jan 2021 Arnaud Boutillon, Bhushan Borotikar, Christelle Pons, Valérie Burdin, Pierre-Henri Conze

Automatic segmentation of the musculoskeletal system in pediatric magnetic resonance (MR) images is a challenging but crucial task for morphological evaluation in clinical practice.

Anatomy Segmentation

Efficient embedding network for 3D brain tumor segmentation

no code implementations22 Nov 2020 Hicham Messaoudi, Ahror Belaid, Mohamed Lamine Allaoui, Ahcene Zetout, Mohand Said Allili, Souhil Tliba, Douraied Ben Salem, Pierre-Henri Conze

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.

Brain Tumor Segmentation Tumor Segmentation

Automatic detection of rare pathologies in fundus photographs using few-shot learning

no code implementations22 Jul 2019 Gwenolé Quellec, Mathieu Lamard, Pierre-Henri Conze, Pascale Massin, Béatrice Cochener

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.

One-Shot Learning Transfer Learning

Unsupervised learning-based long-term superpixel tracking

no code implementations25 Feb 2019 Pierre-Henri Conze, Florian Tilquin, Mathieu Lamard, Fabrice Heitz, Gwenolé Quellec

Finding correspondences between structural entities decomposing images is of high interest for computer vision applications.

Superpixels Video Object Tracking

Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders

no code implementations6 Jan 2019 Pierre-Henri Conze, Sylvain Brochard, Valérie Burdin, Frances T. Sheehan, Christelle Pons

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.

Management MRI segmentation +1

Adaptive strategy for superpixel-based region-growing image segmentation

no code implementations17 Mar 2018 Mahaman Sani Chaibou, Pierre-Henri Conze, Karim Kalti, Basel Solaiman, Mohamed Ali Mahjoub

From an initial contour-constrained over-segmentation of the input image, the image segmentation is achieved by iteratively merging similar superpixels into regions.

Image Segmentation Segmentation +2

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