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Works23 from public data
- Learning Federated Visual Prompt in Null Space for MRI Reconstruction77
FedPR is a new federated paradigm that adopts a powerful pre-trained model while only learning and communicating the prompts with few learnable parameters, thereby significantly reducing communication costs and achieving competitive performance on limited local data.
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- Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge28
The authors describe the Federated Tumor Segmentation (FeTS) challenge for the decentralised benchmarking of FL algorithms and evaluation of Healthcare AI algorithm generalizability in real-world cancer imaging datasets.
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- Optical coherence tomography choroidal enhancement using generative deep learning14
The DL generative model successfully generated realistic enhanced SDOCT data that is indistinguishable from SSOCT images providing improved visualization of the choroid, enabling accurate measurements of choroidal metrics previously limited by the imaging depth constraints of SDOCT.
- mHealth App to Facilitate Remote Care for Patients With COVID-19: Rapid Development of the DrCovid+ App14
The rapid development and implementation of DrCovid+ allowed for timely clinical care management for patients with COVID-19 and facilitated early patient hospital discharge and continuity of care while addressing issues relating to data security and labor-, time-, and cost-effectiveness.
- Text to Image for Multi-Label Image Recognition With Joint Prompt-Adapter Learning9
T2I-PAL offers significant advantages: it eliminates the need for fully semantically annotated training images, thereby reducing the manual annotation workload, and it preserves the intrinsic mode of the CLIP model, allowing for seamless integration with any existing CLIP framework.
- Class Balance Matters to Active Class-Incremental Learning9
The Active Class-Incremental Learning (ACIL) is introduced, to select the most informative samples from the unlabeled pool to effectively train an incremental learner, aiming to maximize the performance of the resulting model.
- SRDiffusion: Accelerate Video Diffusion Inference via Sketching-Rendering Cooperation7
This paper proposes SRDiffusion, a novel framework that leverages collaboration between large and small models to reduce inference cost, and demonstrates that the method outperforms existing approaches for scalable video generation.
- Semi-rPPG: Semi-Supervised Remote Physiological Measurement With Curriculum Pseudo-Labeling7
A novel semi-supervised learning (SSL) method named semi-rPPG that combines curriculum pseudo-labeling and consistency regularization is proposed to extract intrinsic physiological features from unlabeled data without impairing the model from noises.
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- Improving Learning of New Diseases Through Knowledge-Enhanced Initialization for Federated Adapter Tuning3
This work introduces Federated Knowledge-Enhanced Initialization (FedKEI), a novel framework that leverages cross-client and cross-task transfer from past knowledge to generate informed initializations for learning new tasks with adapters.
- Deep learning for predicting myopia severity classification method2
A deep learning model is proposed, X-ENet, which combines the advantages of depthwise separable convolution and dynamic convolution to classify different severities of myopia, and significantly outperforms existing conventional deep learning models in terms of accuracy.
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- Transforming Assessment of Behavioral Impairment: The Role of Facial AI Technologies–
The preliminary findings support HR derived from facial AI technologies as a promising approach in assisting clinicians with screening of behavioral impairments.
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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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