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- Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning217
This paper proposes a novel TPT method, named DiffTPT, which leverages pre-trained diffusion models to generate diverse and informative new data, and incorporates augmented data by both conventional method and pre-trained stable diffusion to exploit their respective merits, improving the model’s ability to adapt to unknown new test data.
- Feed‐forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection212
A novel automatic classification system based on particle swarm optimization and artificial bee colony and three new variants of feed‐forward neural network (FNN), consisting of IABAP‐fNN, ABC‐SPSO‐FNN, and HPA‐Fnn, which was superior to existing state‐of‐the‐art methods in terms of classification accuracy.
- Automated classification of brain images using wavelet-energy and biogeography-based optimization190
The results based on a 5 × 5-fold cross validation showed the performance of the proposed BBO-KSVM was superior to BP-NN, KSVM, and PSO- KSVM in terms of sensitivity and accuracy.
- Multimodal Transformer for Accelerated MR Imaging182
A multi-modal transformer (MTrans) is proposed, which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging and outperforms state-of-the-art methods on fastMRI and real-world clinical datasets.
- Task Transformer Network for Joint MRI Reconstruction and Super-Resolution169
An end-to-end task transformer network (T$^2$Net) is proposed for joint MRI reconstruction and super-resolution, which allows representations and feature transmission to be shared between multiple task to achieve higher-quality, super-resolved and motion-artifacts-free images from highly undersampled and degenerated MRI data.
- Multi-contrast MRI Super-Resolution via a Multi-stage Integration Network143
Extensive experiments demonstrate that the proposed MINet outperforms state-of-the-art multi-contrast MRI SR methods in terms of various metrics and the multi-stage integration module is able to excavate complex interactions among multi-Contrast features at different stages, leading to improved target-image quality.
- Specificity-Preserving Federated Learning for MR Image Reconstruction122
The core idea is to divide the MR reconstruction model into two parts: a globally shared encoder to obtain a generalized representation at the global level, and a client-specific decoder to preserve the domain-specific properties of each client, which is important for collaborative reconstruction when the clients have unique distribution.
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- Visual Programming for Zero-Shot Open-Vocabulary 3D Visual Grounding97
This work proposes a novel visual programming approach for zero-shot open-vocabulary 3DVG, leveraging the capabilities of large language models (LLMs) and develops an innovative language-object correlation module to extend the scope of existing 3D object detectors into open-vocabulary scenarios.
- Sentence-level Prompts Benefit Composed Image Retrieval89
This work demonstrates that learning an appropriate sentence-level prompt for the relative caption (SPRC) is sufficient for achieving effective composed image retrieval and proposes to leverage pretrained V-L models, e.g., BLIP-2, to generate sentence- level prompts.
- 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.
- Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution76
This study proposes a separable attention network (comprising high-intensity priority (HP) attention and low-intensity separation (LS) attention), named SANet, which is the first model to explore a separable attention mechanism that uses the auxiliary contrast to predict the high- and low-intensity regions.
- Brain MRI super-resolution using coupled-projection residual network65
Experiments show that the proposed CPRN achieves superior MRI super-resolution performance compared with the state-of-the-art, and a step-wise connection (CPRN_S) is designed, inspired by the human cognitive process.
- DONet: Dual-Octave Network for Fast MR Image Reconstruction53
The dual-octave network (DONet), which is capable of learning multiscale spatial-frequency features from both the real and imaginary components of MR data, for parallel fast MR image reconstruction, is proposed.
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- A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers31
A comprehensive, data-centric synthesis that reframes the development of Sci-LLMs as a co-evolution between models and their underlying data substrate, and outlines a paradigm shift toward closed-loop systems where autonomous agents based on Sci-LLMs actively experiment, validate, and contribute to a living, evolving knowledge base.
- Achieving flexible fairness metrics in federated medical imaging31
Evaluated across four clinical applications (polyp segmentation, fundus vascular segmentation, cervical cancer segmentation, and skin disease diagnosis), FlexFair outperforms state-of theart methods in both fairness and accuracy.
- Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction31
The Dual-Octave Convolution (Dual-OctConv), which is capable of learning multi-scale spatial-frequency features from both real and imaginary components, for fast parallel MR image reconstruction, shows a strong ability to capture richer representations of MR images, while at the same time greatly reducing the spatial redundancy.
- Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution29
A real-world ×4 VSR dataset is built, namely MVSR4×, where low- and high-resolution videos are captured with different focal length lenses of a smartphone, respectively, and an effective alignment method is proposed, namely EAVSR, which takes the proposed multi-layer adaptive spatial transform network to refine the offsets provided by the pre-trained optical flow estimation network.
- From pretraining to privacy: federated ultrasound foundation model with self-supervised learning28
UltraFedFM surpasses the diagnostic accuracy of mid-level ultrasonographers and matches the performance of expert-level sonographers in the joint diagnosis of 8 common systemic diseases, marking a significant advancement in AI-driven ultrasound imaging for future clinical applications.
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- Towards Instance-adaptive Inference for Federated Learning27
This paper presents a novel FL algorithm, i.e., FedIns, to handle intra-client data heterogeneity by enabling instance-adaptive inference in the FL framework, and resorts to a parameter-efficient fine-tuning method, i.e., scale and shift deep features (SSF) upon a pre-trained model.
- Automated Classification of Brain MR Images Using Wavelet-Energy and Support Vector Machines25
The proposed wavelet-energy based new approach for automated classification of MR human brain images showed its performance was comparable with state-of-the-art algorithms and provided a new means to detect features indicative of abnormal brains.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. 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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