H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation

Shi, Jun; Kan, Hongyu; Ruan, Shulan; Zhu, Ziqi; Zhao, Minfan; Qiao, Liang; Wang, Zhaohui; An, Hong; Xue, Xudong

doi:10.1007/978-3-031-43901-8_66

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation

Cited by 5 publications

References 33 publications

LM-UNet: Whole-Body PET-CT Lesion Segmentation with Dual-Modality-Based Annotations Driven by Latent Mamba U-Net

LM-UNet: Whole-Body PET-CT Lesion Segmentation with Dual-Modality-Based Annotations Driven by Latent Mamba U-Net

Multimodal MRI segmentation of key structures for microvascular decompression via knowledge-driven mutual distillation and topological constraints

A transformer-based convolutional method to model inverse cascade in forced two-dimensional turbulence

Contact Info

Product

Resources

About