TL;DR
RLU-Net-GD utilizes residual connections in the encoder to enhance feature propagation and mitigate gradient vanishing, while deep supervision in the guided decoder, employing a weighted guided loss, ensures the extraction of salient features, resulting in superior segmentation performance compared to state-of-the-art methods.
A lightweight Vision Transformer UNet is proposed that combines the hierarchical feature extraction capability of UNet with the global context modeling of Vision Transformers, enabling effective learning of both local and global features while maintaining computational efficiency with only 2.6 million trainable paramet...
The suggested SwinNet Plus model, the new hybrid architecture of deep learning that combines the complementary advantages of Swin Transformers and Convolutional Neural Networks, is suggested to strengthen accurate brain tumor localization and becomes an attractive instrument in clinical determination support systems.
Evaluation on the BraTS GOAT validation dataset demonstrated that the proposed Multi-Stage Dynamic Prompt nnU-Net outperformed the baseline nnU Net across the majority of evaluated metrics and tumor subregions.
SERA-Net, a convolutional architecture for brain tumor classification using MRI scans is presented, which provides a scalable design that can be adapted to different domains with limited modifications and was observed to outperform six pretrained CNN baselines by more than 5–10%, while using substantially fewer paramet...
A 3D U-Net-based segmentation framework, NeuroFocusNet, which synchronizes spatial attention, channel recalibration, and attention-gated skip connections within a single volumetric encoder-decoder model is proposed, which is encouraging when considering the feasibility of using NeuroFocusNet as a technically promising...
VGG16-MCA UNet is presented, a hybrid architecture pairing an ImageNet-pretrained VGG16 encoder with a decoder in which a Multi-Channel Attention (MCA) module recalibrates features after each skip-connection fusion, trained with the Focal Tversky loss to counter severe foreground-background imbalance.
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