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

• URL: http://arxiv.org/abs/2405.04064v2
• Date: Thu, 9 May 2024 12:26:45 GMT
• Title: MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation
• Authors: Yanli Yuan, Bingbing Wang, Chuan Zhang, Jingyi Xu, Ximeng Liu, Liehuang Zhu,
• Abstract summary: We propose a new segmentation framework based on attention mechanisms, named MFA-Net. The proposed framework can learn more meaningful feature maps among multiple scales and result in more accurate automatic segmentation.
• Score: 36.837642256513426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• MSA$^2$Net: Multi-scale Adaptive Attention-guided Network for Medical Image Segmentation [8.404273502720136]
  We introduce MSA$2$Net, a new deep segmentation framework featuring an expedient design of skip-connections. We propose a Multi-Scale Adaptive Spatial Attention Gate (MASAG) to ensure that spatially relevant features are selectively highlighted. Our MSA$2$Net outperforms state-of-the-art (SOTA) works or matches their performance.
  arXiv  Detail & Related papers  (2024-07-31T14:41:10Z)
• Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention [1.1155836879100416]
  We propose a Modality-agnostic Domain Generalizable Network (MADGNet) for medical image segmentation. MFMSA block refines the process of spatial feature extraction, particularly in capturing boundary features. E-SDM mitigates information loss in multi-task learning with deep supervision.
  arXiv  Detail & Related papers  (2024-05-10T07:34:36Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images [50.76668288066681]
  We present a scale-aware super-resolution network to adaptively segment lesions of various sizes from low-resolution medical images. Our proposed network achieved consistent improvements compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-30T14:25:55Z)
• M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation [73.10707675345253]
  We propose a general multi-scale in multi-scale subtraction network (M$2$SNet) to finish diverse segmentation from medical image. Our method performs favorably against most state-of-the-art methods under different evaluation metrics on eleven datasets of four different medical image segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-20T06:26:49Z)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images [0.696194614504832]
  Medical image segmentation assists in computer-aided diagnosis, surgeries, and treatment. We proposed an generalization-Decoder Network, Quick Attention Module and a Multi Loss Function. We evaluate the capability of our proposed network on two publicly available datasets for medical image segmentation MoNuSeg and GlaS.
  arXiv  Detail & Related papers  (2022-09-01T21:10:00Z)
• UNet#: A UNet-like Redesigning Skip Connections for Medical Image Segmentation [13.767615201220138]
  We propose a novel network structure combining dense skip connections and full-scale skip connections, named UNet-sharp (UNet#) for its shape similar to symbol #. The proposed UNet# can aggregate feature maps of different scales in the decoder sub-network and capture fine-grained details and coarse-grained semantics from the full scale.
  arXiv  Detail & Related papers  (2022-05-24T03:40:48Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation [10.979393806308648]
  We propose a novel architecture called MSRF-Net, which is specially designed for medical image segmentation tasks. MSRF-Net is able to exchange multi-scale features of varying receptive fields using a dual-scale dense fusion block (DSDF) Our DSDF block can exchange information rigorously across two different resolution scales, and our MSRF sub-network uses multiple DSDF blocks in sequence to perform multi-scale fusion.
  arXiv  Detail & Related papers  (2021-05-16T15:19:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.