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Found 23 papers, 2 papers with code
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.
Generalizing deep learning models for medical image classification
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.
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.
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.
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.
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.
Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT images
Then 3D nnU-Net architecture was adopted to automatic segmentation of primary tumor and lymph nodes synchronously. Based on predicted segmentation, ten conventional features and 346 standardized radiomics features were extracted for each patient.
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.
White-box Membership Attack Against Machine Learning Based Retinopathy Classification
From a privacy perspective in our use case where a diabetic retinopathy classification model is given to partners that have at their disposal images along with patients' identifiers, inferring the membership status of a data sample can help to state if a patient has contributed or not to the training of the model.
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.
