Volumetric Medical Image Segmentation
17 papers with code • 1 benchmarks • 2 datasets
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Use these libraries to find Volumetric Medical Image Segmentation models and implementationsLatest papers with no code
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation
D-Net is able to effectively utilize a multi-scale large receptive field and adaptively harness global contextual information.
Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priors
In this paper, we propose a scribble-based volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image segmentation and aims to its improve boundary prediction.
Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder
Masked autoencoder (MAE) is a promising self-supervised pre-training technique that can improve the representation learning of a neural network without human intervention.
Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation
Recently, the advent of vision Transformer (ViT) has brought substantial advancements in 3D dataset benchmarks, particularly in 3D volumetric medical image segmentation (Vol-MedSeg).
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling
In this study, we introduce Masked Autoencoding and Pseudo-Labeling Segmentation (MAPSeg), a $\textbf{unified}$ UDA framework with great versatility and superior performance for heterogeneous and volumetric medical image segmentation.
Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift
At the saddle point of the underlying objective, the weights assign label-dense samples to the supervised loss and label-sparse samples to the unsupervised consistency regularization.
Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma
It can be separated into two regions, intrameatal and extrameatal respectively corresponding to being inside or outside the inner ear canal.
Distributed Contrastive Learning for Medical Image Segmentation
However, when adopting CL in FL, the limited data diversity on each site makes federated contrastive learning (FCL) ineffective.
Implicit U-Net for volumetric medical image segmentation
U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net architecture to 3D images.
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.