MM-3D Unet: development of a lightweight breast cancer tumor segmentation network utilizing multi-task and depthwise separable convolution

MM-3D Unet: development of a lightweight breast cancer tumor segmentation network utilizing multi-task and depthwise separable convolution

Background and objectivesThis paper introduces a novel lightweight MM-3DUNet (Multi-task Mobile 3D UNet) network designed for efficient and accurate segmentation of breast cancer tumors masses from MRI images, which leverages depth-wise separable convolutions, channel expansion units, and auxiliary...

Full description

Saved in:
Bibliographic Details
Main Authors: Xian Wang, Wenzhi Zeng, Junzeng Xu, Senhao Zhang, Yuexing Gu, Benhui Li, Xueyang Wang
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Oncology
Subjects:
multi-task mobile 3D UNet
dynamic contrast enhanced MRI
breast cancer images segmentation
resource-constrained environments
convolutional neural networks
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2025.1563959/full
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.frontiersin.org/articles/10.3389/fonc.2025.1563959/full

Similar Items