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- Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study50
Among diverse multi-ethnic populations with diabetes, a retinal image-based AI-deep learning system showed its potential for detecting DKD and differentiating isolated diabetic nephropathy from NDKD in clinical practice.
- Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes40
There is potential for DLA using retinal images as a screening adjunct for DKD among individuals with diabetes, and this can value-add to existing DLA systems which diagnose diabetic retinopathy fromretinal images, facilitating primary screening for DKd.
- Leveraging Old Knowledge to Continually Learn New Classes in Medical Images16
A framework that comprises of a dynamic architecture with expanding representations to preserve previously learned features and accommodate new features and a training procedure alternating between two objectives to balance the learning of new features while maintaining the model’s performance on old classes is proposed.
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- Multi-Modal Continual Learning via Cross-Modality Adapters and Representation Alignment with Knowledge Preservation1
This work proposes a pre-trained model-based framework that includes a novel cross-modality adapter with a mixture-of-experts structure to facilitate effective integration of multi-modal information across tasks, and introduces a representation alignment loss that fosters learning of robust multi-modal representations.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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