A deep learning model, Up-Net, is proposed to overcome semantic inconsistency issue. A lightweight up-concatenation structure and metric learning strategy is ...
By introducing depthwise separable convolution and attention mechanism into U-shaped architecture, a novel lightweight neural network (DSCA-Net) is proposed ...
Mar 19, 2023 · Up-Net obtains better semantics consistency and successfully avoids the overfilled flaw compared to the result of DeepLabv3+.
Oct 1, 2021 · The latest deep neural networks for medical segmentation typically utilize transposed convolutional filters and atrous convolutional filters ...
People also ask
What is the best model for semantic segmentation?
What is semantic image segmentation?
Which architecture is best for medical image segmentation?
What are image segmentation techniques in medical imaging?
We apply it in semantic segmentation of 2D RGB images by eval- uating and enhancing the cross-view consistency while vary- ing view direction of any given ...
Oct 17, 2024 · We propose a novel semi-supervised medical image segmentation framework, termed SemSim, which addresses the intra- and cross-image semantic inconsistency ...
This paper proposes a semi-supervised multi-modality segmentation framework based on pre-trained SAM-Med3D model to align the information of different modal ...
For easy evaluation and fair comparison, we are trying to build a semi-supervised medical image segmentation benchmark to boost the semi-supervised learning ...
Jul 31, 2024 · Mix-up is a key technique for consistency regularization-based semi-supervised learning methods, generating strong-perturbed samples for strong- ...
Abstract. Integrating multi-modal data to promote medical image analysis has recently gained great attention. This paper presents a novel scheme to learn ...