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.
nnformer: Interleaved transformer for volumetric segmentation
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
A unified autoregressive vision-language framework integrates segmentation, detection, and appearance reasoning for CT images via task-routing tokens and progressive refinement, with gains on public benchmarks.
MHMamba combines a U-Net with multi-head Mamba, channel calibration, and adaptive skip fusion to improve 3D brain tumor segmentation accuracy and small-lesion sensitivity on BraTS datasets while retaining linear complexity.
PRC-TP combines an nnFormer network trained on Monte Carlo simulations with Model-consistent Texture Re-Injection to correct positron range effects in 82Rb PET while restoring acquisition-consistent texture.
FM-BFF-Net combines focal modulation attention with bidirectional encoder-decoder fusion in a CNN-transformer architecture and reports higher Dice and Jaccard scores than recent methods across eight medical image datasets.
SwinUNETR outperforms 3D UNet with Dice scores up to 0.902 on larger gland subsets using mixed-cohort five-fold training, while UNETR performs poorly on the same subsets.
citing papers explorer
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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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Segmentation, Detection and Explanation: A Unified Framework for CT Appearance Reasoning
A unified autoregressive vision-language framework integrates segmentation, detection, and appearance reasoning for CT images via task-routing tokens and progressive refinement, with gains on public benchmarks.
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MHMamba: Multi-Head Mamba for 3D Brain Tumor Segmentation
MHMamba combines a U-Net with multi-head Mamba, channel calibration, and adaptive skip fusion to improve 3D brain tumor segmentation accuracy and small-lesion sensitivity on BraTS datasets while retaining linear complexity.
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A Positron Range Correction with Texture Preservation Framework in PET Imaging
PRC-TP combines an nnFormer network trained on Monte Carlo simulations with Model-consistent Texture Re-Injection to correct positron range effects in 82Rb PET while restoring acquisition-consistent texture.
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Focal Modulation and Bidirectional Feature Fusion Network for Medical Image Segmentation
FM-BFF-Net combines focal modulation attention with bidirectional encoder-decoder fusion in a CNN-transformer architecture and reports higher Dice and Jaccard scores than recent methods across eight medical image datasets.