MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation

Autor:	Yuan, Yanli, Wang, Bingbing, Zhang, Chuan, Xu, Jingyi, Liu, Ximeng, Zhu, Liehuang
Rok vydání:	2024
Předmět:	Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Segmentation of organs of interest in medical CT images is beneficial for diagnosis of diseases. Though recent methods based on Fully Convolutional Neural Networks (F-CNNs) have shown success in many segmentation tasks, fusing features from images with different scales is still a challenge: (1) Due to the lack of spatial awareness, F-CNNs share the same weights at different spatial locations. (2) F-CNNs can only obtain surrounding information through local receptive fields. To address the above challenge, we propose a new segmentation framework based on attention mechanisms, named MFA-Net (Multi-Scale Feature Fusion Attention Network). The proposed framework can learn more meaningful feature maps among multiple scales and result in more accurate automatic segmentation. We compare our proposed MFA-Net with SOTA methods on two 2D liver CT datasets. The experimental results show that our MFA-Net produces more precise segmentation on images with different scales. Comment: Paper accepted in Human-Centric Representation Learning workshop at AAAI 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.04064 Zobrazit plný text záznamu View this record from Arxiv