Transformers in medical imaging: A survey
Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …
successfully applied to several computer vision problems, achieving state-of-the-art results …
[HTML][HTML] TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers
Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-
Net face limitations in modeling long-range dependencies. To address this, Transformers …
Net face limitations in modeling long-range dependencies. To address this, Transformers …
Sparse Dynamic Volume TransUNet with multi-level edge fusion for brain tumor segmentation
Abstract 3D MRI Brain Tumor Segmentation is of great significance in clinical diagnosis and
treatment. Accurate segmentation results are critical for localization and spatial distribution …
treatment. Accurate segmentation results are critical for localization and spatial distribution …
Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge
Automatic segmentation of abdominal organs in CT scans plays an important role in clinical
practice. However, most existing benchmarks and datasets only focus on segmentation …
practice. However, most existing benchmarks and datasets only focus on segmentation …
CKD-TransBTS: clinical knowledge-driven hybrid transformer with modality-correlated cross-attention for brain tumor segmentation
Brain tumor segmentation (BTS) in magnetic resonance image (MRI) is crucial for brain
tumor diagnosis, cancer management and research purposes. With the great success of the …
tumor diagnosis, cancer management and research purposes. With the great success of the …
3d transunet: Advancing medical image segmentation through vision transformers
Medical image segmentation plays a crucial role in advancing healthcare systems for
disease diagnosis and treatment planning. The u-shaped architecture, popularly known as …
disease diagnosis and treatment planning. The u-shaped architecture, popularly known as …
Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation
Single-modality medical images generally do not contain enough information to reach an
accurate and reliable diagnosis. For this reason, physicians commonly rely on multimodal …
accurate and reliable diagnosis. For this reason, physicians commonly rely on multimodal …
One model to synthesize them all: Multi-contrast multi-scale transformer for missing data imputation
Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical practice as each
contrast provides complementary information. However, the availability of each imaging …
contrast provides complementary information. However, the availability of each imaging …
Bridged-U-Net-ASPP-EVO and deep learning optimization for brain tumor segmentation
Brain tumor segmentation from Magnetic Resonance Images (MRI) is considered a big
challenge due to the complexity of brain tumor tissues, and segmenting these tissues from …
challenge due to the complexity of brain tumor tissues, and segmenting these tissues from …
High-resolution Swin transformer for automatic medical image segmentation
The resolution of feature maps is a critical factor for accurate medical image segmentation.
Most of the existing Transformer-based networks for medical image segmentation adopt a U …
Most of the existing Transformer-based networks for medical image segmentation adopt a U …