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Found 12 papers, 6 papers with code
Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation
As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i. e. Med-DANet) has achieved a favorable accuracy and efficiency trade-off by dynamically selecting a suitable 2D candidate model from the pre-defined model bank for different slices.
CM-MaskSD: Cross-Modality Masked Self-Distillation for Referring Image Segmentation
Referring image segmentation (RIS) is a fundamental vision-language task that intends to segment a desired object from an image based on a given natural language expression.
FreMIM: Fourier Transform Meets Masked Image Modeling for Medical Image Segmentation
The research community has witnessed the powerful potential of self-supervised Masked Image Modeling (MIM), which enables the models capable of learning visual representation from unlabeled data.
Med-Tuning: Parameter-Efficient Transfer Learning with Fine-Grained Feature Enhancement for Medical Volumetric Segmentation
In this paper, we present the study on parameter-efficient transfer learning for medical volumetric segmentation and propose a new framework named Med-Tuning based on intra-stage feature enhancement and inter-stage feature interaction.
MF2-MVQA: A Multi-stage Feature Fusion method for Medical Visual Question Answering
There is a key problem in the medical visual question answering task that how to effectively realize the feature fusion of language and medical images with limited datasets.
Positive-Negative Equal Contrastive Loss for Semantic Segmentation
The contextual information is critical for various computer vision tasks, previous works commonly design plug-and-play modules and structural losses to effectively extract and aggregate the global context.
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation
For 3D medical image (e. g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly.
Attention guided global enhancement and local refinement network for semantic segmentation
First, commonly used upsampling methods in the decoder such as interpolation and deconvolution suffer from a local receptive field, unable to encode global contexts.
Category Guided Attention Network for Brain Tumor Segmentation in MRI
In this model, we design a Supervised Attention Module (SAM) based on the attention mechanism, which can capture more accurate and stable long-range dependency in feature maps without introducing much computational cost.
TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images
Different from TransBTS, the proposed TransBTSV2 is not limited to brain tumor segmentation (BTS) but focuses on general medical image segmentation, providing a stronger and more efficient 3D baseline for volumetric segmentation of medical images.
TransBTS: Multimodal Brain Tumor Segmentation Using Transformer
To capture the local 3D context information, the encoder first utilizes 3D CNN to extract the volumetric spatial feature maps.
3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI
In this work, we aim to segment brain MRI volumes.
