TransSplat formulates language-driven 3D Gaussian Splatting editing as a multi-view unbalanced semantic transport problem, achieving better cross-view consistency and local editing precision than prior fusion-based methods on 8 benchmark scenes.
Missformer: An effective medical image segmentation transformer
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MS-DKC is a dataset knowledge card framework that maps image, morphology, supervision, context, and risk descriptors to design priors and failure modes, shown to produce dataset-specific model adaptations with improved metrics on DRIVE, ISIC2018, and ACDC.
Primus and PrimusV2 are Transformer-centric models that match or exceed nnU-Net and top CNNs on nine 3D medical segmentation datasets by enforcing attention usage.
SwInception augments Swin transformers with Inception-style multi-branch convolutions in feed-forward layers plus decoder modifications, reporting gains over prior backbones on eleven medical segmentation datasets including MSD and BTCV benchmarks.
MSLAU-Net proposes a hybrid CNN-Transformer architecture using multi-scale linear attention and lightweight top-down aggregation that outperforms prior methods on medical segmentation benchmarks across three modalities.
Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.
SwinTextUNet integrates CLIP text guidance into Swin U-Net via cross-attention and convolutional fusion, achieving 86.47% Dice and 78.2% IoU on QaTaCOV19 medical image segmentation.
citing papers explorer
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RF-HiT: Rectified Flow Hierarchical Transformer for General Medical Image Segmentation
TransSplat formulates language-driven 3D Gaussian Splatting editing as a multi-view unbalanced semantic transport problem, achieving better cross-view consistency and local editing precision than prior fusion-based methods on 8 benchmark scenes.
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MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models
MS-DKC is a dataset knowledge card framework that maps image, morphology, supervision, context, and risk descriptors to design priors and failure modes, shown to produce dataset-specific model adaptations with improved metrics on DRIVE, ISIC2018, and ACDC.
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Primus: Enforcing Attention Usage for 3D Medical Image Segmentation
Primus and PrimusV2 are Transformer-centric models that match or exceed nnU-Net and top CNNs on nine 3D medical segmentation datasets by enforcing attention usage.
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SwInception -- Local Attention Meets Convolutions
SwInception augments Swin transformers with Inception-style multi-branch convolutions in feed-forward layers plus decoder modifications, reporting gains over prior backbones on eleven medical segmentation datasets including MSD and BTCV benchmarks.
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MSLAU-Net: A Hybrid CNN-Transformer Network for Medical Image Segmentation
MSLAU-Net proposes a hybrid CNN-Transformer architecture using multi-scale linear attention and lightweight top-down aggregation that outperforms prior methods on medical segmentation benchmarks across three modalities.
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APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms
Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.
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SwinTextUNet: Integrating CLIP-Based Text Guidance into Swin Transformer U-Nets for Medical Image Segmentation
SwinTextUNet integrates CLIP text guidance into Swin U-Net via cross-attention and convolutional fusion, achieving 86.47% Dice and 78.2% IoU on QaTaCOV19 medical image segmentation.