no code implementations • 19 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.
no code implementations • 23 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.
no code implementations • 10 Apr 2024 • Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho, Yihao Li, Hugo Le Boité, Ramin Tadayoni, Pascal Massin, Béatrice Cochener, Alireza Rezaei, Ikram Brahim, Gwenolé Quellec, Mathieu Lamard
This work proposes a novel framework for analyzing disease progression using time-aware neural ordinary differential equations (NODE).
no code implementations • 24 Mar 2024 • Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho, Yihao Li, Alireza Rezaei, Hugo Le Boité, Ramin Tadayoni, Pascal Massin, Béatrice Cochener, Ikram Brahim, Gwenolé Quellec, Mathieu Lamard
Our results demonstrated the relevancy of both time-aware position embedding and masking strategies based on disease progression knowledge.
no code implementations • 18 Mar 2024 • Sarah Matta, Mathieu Lamard, Philippe Zhang, Alexandre Le Guilcher, Laurent Borderie, Béatrice Cochener, Gwenolé Quellec
Two main shift types can occur over time: 1) covariate shift mainly arising due to updates to medical equipment and 2) concept shift caused by inter-grader variability.
no code implementations • 10 Jan 2024 • Mostafa El Habib Daho, Yihao Li, Rachid Zeghlache, Hugo Le Boité, Pierre Deman, Laurent Borderie, Hugang Ren, Niranchana Mannivanan, Capucine Lepicard, Béatrice Cochener, Aude Couturier, Ramin Tadayoni, Pierre-Henri Conze, Mathieu Lamard, Gwenolé Quellec
A straightforward solution to this task is a 3-D neural network classifier.
1 code implementation • 8 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.
no code implementations • 16 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.
no code implementations • 16 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.
no code implementations • 3 Oct 2023 • Mostafa El Habib Daho, Yihao Li, Rachid Zeghlache, Yapo Cedric Atse, Hugo Le Boité, Sophie Bonnin, Deborah Cosette, Pierre Deman, Laurent Borderie, Capucine Lepicard, Ramin Tadayoni, Béatrice Cochener, Pierre-Henri Conze, Mathieu Lamard, Gwenolé Quellec
Diabetic Retinopathy (DR), a prevalent and severe complication of diabetes, affects millions of individuals globally, underscoring the need for accurate and timely diagnosis.
1 code implementation • 21 Nov 2022 • Yihao Li, Rachid Zeghlache, Ikram Brahim, Hui Xu, Yubo Tan, Pierre-Henri Conze, Mathieu Lamard, Gwenolé Quellec, Mostafa El Habib Daho
Diabetic Retinopathy (DR) is a severe complication of diabetes that can cause blindness.
no code implementations • 2 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.
no code implementations • 2 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.
no code implementations • 2 Sep 2022 • Ikram Brahim, Mathieu Lamard, Anas-Alexis Benyoussef, Pierre-Henri Conze, Béatrice Cochener, Divi Cornec, Gwenolé Quellec
With a prevalence of 5 to 50%, Dry Eye Disease (DED) is one of the leading reasons for ophthalmologist consultations.
no code implementations • 13 Aug 2020 • Gwenolé Quellec, Hassan Al Hajj, Mathieu Lamard, Pierre-Henri Conze, Pascale Massin, Béatrice Cochener
In recent years, Artificial Intelligence (AI) has proven its relevance for medical decision support.
no code implementations • 27 Feb 2020 • Yutong Yan, Pierre-Henri Conze, Gwenolé Quellec, Mathieu Lamard, Béatrice Cochener, Gouenou Coatrieux
In this work, we present a two-stage multi-scale pipeline that provides accurate mass contours from high-resolution full mammograms.
no code implementations • 22 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.
no code implementations • 12 Jun 2019 • Gwenolé Quellec, Mathieu Lamard, Bruno Lay, Alexandre Le Guilcher, Ali Erginay, Béatrice Cochener, Pascale Massin
The purpose of this study is to evaluate the performance of the OphtAI system for the automatic detection of referable diabetic retinopathy (DR) and the automatic assessment of DR severity using color fundus photography.
no code implementations • 25 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.
no code implementations • 4 Oct 2017 • Hassan Al Hajj, Mathieu Lamard, Pierre-Henri Conze, Béatrice Cochener, Gwenolé Quellec
Tool usage is monitored in videos recorded either through a microscope (cataract surgery) or an endoscope (cholecystectomy).
no code implementations • 22 Oct 2016 • Gwenolé Quellec, Katia Charrière, Yassine Boudi, Béatrice Cochener, Mathieu Lamard
However, deep learning algorithms, including the popular ConvNets, are black boxes: little is known about the local patterns analyzed by ConvNets to make a decision at the image level.
no code implementations • 18 Oct 2016 • Katia Charrière, Gwenolé Quellec, Mathieu Lamard, David Martiano, Guy Cazuguel, Gouenou Coatrieux, Béatrice Cochener
The automatic analysis of the surgical process, from videos recorded during surgeries, could be very useful to surgeons, both for training and for acquiring new techniques.
no code implementations • 19 Sep 2016 • Hassan Al Hajj, Gwenolé Quellec, Mathieu Lamard, Guy Cazuguel, Béatrice Cochener
To this end, the proposed solution is divided into two main parts: one to detect the instruments at the beginning of the surgery and one to update the list of instruments every time a change is detected in the scene.