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- EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention912
This paper proposes a family of high-speed vision transformers named Efficient ViT, and designs a new building block with a sandwich layout, i.e., using a single memory-bound MHSA between efficient FFN layers, which improves memory efficiency while enhancing channel communication.
- Selective Feature Aggregation Network with Area-Boundary Constraints for Polyp Segmentation445
This work proposes a novel selective feature aggregation network with the area and boundary constraints and introduces three up-concatenations between encoder and decoders and embedding Selective Kernel Modules into convolutional layers which can adaptively extract features from different size of kernels.
- CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection391
The CLIP-Driven Universal Model is proposed, which incorporates text embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models and is computationally more efficient compared with dataset-specific models, generalized better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks.
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- Measurement of Branching Fractions for B→ηcK(*) Decays229
Measurements of branching fractions for charged and neutral B-->eta( c)K decays where the eta(c) meson is reconstructed in the K(0)(S)K+/-pi(-/+), K+K-pi(0), K(*0)K- pi(+), and pp; decay channels are reported.
- Integrated lithium niobate microwave photonic processing engine219
The image edge detector is leveraged to enable a photonic-assisted image segmentation model that could effectively outline the boundaries of melanoma lesion in medical diagnostic images, achieving orders of magnitude faster processing speed and lower power consumption than conventional electronic processors.
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- Deep learning for polyp recognition in wireless capsule endoscopy images194
The comprehensive results have demonstrated that the proposed SSAEIM can provide descriptive characterization for WCE images and recognize polyps in a WCE video accurately and could be further utilized in the clinical trials to help physicians from the tedious image reading work.
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- DICCCOL: Dense Individualized and Common Connectivity-Based Cortical Landmarks172
This work reports a dense and consistent map of 358 cortical landmarks, named Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOLs), defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging data.
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- Ground-Aware Monocular 3D Object Detection for Autonomous Driving161
This work identifies how the ground plane provides additional clues in depth reasoning in 3D detection in driving scenes and improves the processing of 3D anchors and introduces a novel neural network module to fully utilize such application-specific priors in the framework of deep learning.
- Source free domain adaptation for medical image segmentation with fourier style mining158
A novel source free domain adaptation framework with fourier style mining, where only a well-trained source segmentation model is available for the adaptation to the target domain, and delivers impressive performance compared with state-of-the-art domain adaptation methods.
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- Causal Disentanglement Domain Generalization for time-series signal fault diagnosis136
A DGFD method named Causal Disentanglement Domain Generalization (CDDG), which can reestablish the data-generating process by disentangling time-series data into the causal factors (fault-related representation) and no-casual factors (domain-related representation) with a structural causal model.
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- Prostate cancer classification with multiparametric MRI transfer learning model133
The experiment results showed that the proposed MPTL model can learn discriminative features in prostate images and classify the cancer accurately, and could be further applied in the clinical practice to provide valuable information for cancer treatment and precision medicine.
- Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT125
The novel paradigm for toxicity prediction by leveraging the enormous potential of deep learning and go beyond the existing dose/volume histogram-based metric is proposed, which offers clinically accurate tools for hepatobiliary toxicity prediction and automatic identification of anatomical regions that are critical to spare during SBRT.
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- A Heterogeneous Group CNN for Image Super-Resolution107
A heterogeneous group SR CNN (HGSRCNN) via leveraging structure information of different types to obtain a high-quality image and a parallel upsampling mechanism is developed to train a blind SR model.
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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 2026-10-14. 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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