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Found 5 papers, 3 papers with code
MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images
Existing multi-modal learning methods on fundus and OCT images mostly require both modalities to be available and strictly paired for training and testing, which appears less practical in clinical scenarios.
Fundus-Enhanced Disease-Aware Distillation Model for Retinal Disease Classification from OCT Images
Our framework enhances the OCT model during training by utilizing unpaired fundus images and does not require the use of fundus images during testing, which greatly improves the practicality and efficiency of our method for clinical use.
Vessel-Promoted OCT to OCTA Image Translation by Heuristic Contextual Constraints
Our TransPro method is primarily driven by two novel ideas that have been overlooked by prior work.
A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata
The proposed model showed promising performance and could be a potential tool for skin cancer screening.
Ranked #3 on
Skin Lesion Classification
on PAD-UFES-20
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.
