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Found 28 papers, 7 papers with code
Spatiotemporal Representation Learning for Short and Long Medical Image Time Series
Moreover, tracking longer term developments that occur over months or years in evolving processes, such as age-related macular degeneration (AMD), is essential for accurate prognosis.
Deep Multimodal Fusion of Data with Heterogeneous Dimensionality via Projective Networks
The use of multimodal imaging has led to significant improvements in the diagnosis and treatment of many diseases.
3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs
Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models.
Pretrained Deep 2.5D Models for Efficient Predictive Modeling from Retinal OCT
In the field of medical imaging, 3D deep learning models play a crucial role in building powerful predictive models of disease progression.
Transformer-based end-to-end classification of variable-length volumetric data
Consequently, the accumulated positional information in each positional embedding can be generalized to the neighbouring slices, even for high-resolution volumes at the test time.
Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation
Moreover, the proposed SSL method allows further improvement of this performance by up to 23%, and we show that the SSL is beneficial regardless of the network architecture.
Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT
We develop a Deep Learning (DL) model to predict the future risk of conversion of an eye from iAMD to nAMD from its current OCT scan.
Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration
Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly.
Learning Spatio-Temporal Model of Disease Progression with NeuralODEs from Longitudinal Volumetric Data
Our method represents a time-invariant physical process and solves a large-scale problem of modeling temporal pixel-level changes utilizing NeuralODEs.
Segmentation of Bruch's Membrane in retinal OCT with AMD using anatomical priors and uncertainty quantification
Bruch's membrane (BM) segmentation on optical coherence tomography (OCT) is a pivotal step for the diagnosis and follow-up of age-related macular degeneration (AMD), one of the leading causes of blindness in the developed world.
Data-centric AI approach to improve optic nerve head segmentation and localization in OCT en face images
The automatic detection and localization of anatomical features in retinal imaging data are relevant for many aspects.
Metadata-enhanced contrastive learning from retinal optical coherence tomography images
This is exacerbated in longitudinal image datasets that repeatedly image the same patient cohort to monitor their disease progression over time.
SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors
Optical coherence tomography (OCT) is a non-invasive 3D modality widely used in ophthalmology for imaging the retina.
TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes
Recent contrastive learning methods achieved state-of-the-art in low label regimes.
Blood vessel segmentation in en-face OCTA images: a frequency based method
The results underline the visual evaluation.
Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT
In medical imaging, there are clinically relevant segmentation tasks where the output mask is a projection to a subset of input image dimensions.
U-Net with spatial pyramid pooling for drusen segmentation in optical coherence tomography
In the second approach, the surrounding retinal layers (outer boundary retinal pigment epithelium (OBRPE) and Bruch's membrane (BM)) are segmented and the remaining space between these two layers is extracted as drusen.
Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning
Longitudinal imaging is capable of capturing the static ana\-to\-mi\-cal structures and the dynamic changes of the morphology resulting from aging or disease progression.
An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans
Supervised deep learning models trained with standard loss functions are usually able to characterize only the most common disease appeareance from a training set, resulting in suboptimal performance and poor generalization when dealing with unseen lesions.
Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography
We also introduce connections between each class-specific branch and the additional decoder to increase the regularization effect of this surrogate task.
Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT
We propose to take advantage of this property using bayesian deep learning, based on the assumption that epistemic uncertainties will correlate with anatomical deviations from a normal training set.
Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
Among the several sources of variability the ML models have to deal with, a major factor is the acquisition device, which can limit the ML model's generalizability.
U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans
In this paper, we introduce a Bayesian deep learning based model for segmenting the photoreceptor layer in pathological OCT scans.
Ranked #4 on
Image Matting
on AIM-500
On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems
In two supervised learning problems, clinical image segmentation and music information classification, the application of our proposed augmented target loss functions increase the accuracy.
Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data
A multi-scale deep denoising autoencoder is trained on healthy images, and a one-class support vector machine identifies anomalies in new data.
Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images
The automatic detection of disease related entities in retinal imaging data is relevant for disease- and treatment monitoring.
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging.
Generative Adversarial Network
Unsupervised Anomaly Detection
Identifying and Categorizing Anomalies in Retinal Imaging Data
The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice.
