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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.
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.
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.
