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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.
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.
