Search Results for author: Béatrice Cochener

Found 17 papers, 0 papers with code

Generalizing deep learning models for medical image classification

no code implementations18 Mar 2024 Matta Sarah, Lamard Mathieu, Zhang Philippe, Alexandre Le Guilcher, Laurent Borderie, Béatrice Cochener, Gwenolé Quellec

Numerous Deep Learning (DL) models have been developed for a large spectrum of medical image analysis applications, which promises to reshape various facets of medical practice.

Image Classification Medical Image Classification

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.

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

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

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

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

Instant automatic diagnosis of diabetic retinopathy

no code implementations12 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.

Deep image mining for diabetic retinopathy screening

no code implementations22 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.

Real-time analysis of cataract surgery videos using statistical models

no code implementations18 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.

Retrieval Video Retrieval

Coarse-to-fine Surgical Instrument Detection for Cataract Surgery Monitoring

no code implementations19 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.

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