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Found 8 papers, 1 papers with code
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.
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.
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.
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.
A Systematic Search over Deep Convolutional Neural Network Architectures for Screening Chest Radiographs
Chest radiographs are primarily employed for the screening of pulmonary and cardio-/thoracic conditions.
Learning Decision Ensemble using a Graph Neural Network for Comorbidity Aware Chest Radiograph Screening
Chest radiographs are primarily employed for the screening of cardio, thoracic and pulmonary conditions.
A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms
Our method performed well on the task of localization of masses with an average Precision/Recall of 0. 76/0. 80 and acheived an average AUC of 0. 91 on the imagelevel classification task using a five-fold cross-validation on the INbreast dataset.
