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Found 21 papers, 2 papers with code
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).
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.
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.
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.
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.
Instant automatic diagnosis of diabetic retinopathy
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.
Unsupervised learning-based long-term superpixel tracking
Finding correspondences between structural entities decomposing images is of high interest for computer vision applications.
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).
Deep image mining for diabetic retinopathy screening
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
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.
Coarse-to-fine Surgical Instrument Detection for Cataract Surgery Monitoring
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.
