EAA-Net: Rethinking the Autoencoder Architecture with Intra-class Features for Medical Image Segmentation

• URL: http://arxiv.org/abs/2208.09197v1
• Date: Fri, 19 Aug 2022 07:42:55 GMT
• Title: EAA-Net: Rethinking the Autoencoder Architecture with Intra-class Features for Medical Image Segmentation
• Authors: Shiqiang Ma, Xuejian Li, Jijun Tang, Fei Guo
• Abstract summary: We propose a light-weight end-to-end segmentation framework based on multi-task learning, termed Edge Attention autoencoder Network (EAA-Net) Our approach not only utilizes the segmentation network to obtain inter-class features, but also applies the reconstruction network to extract intra-class features among the foregrounds. Experimental results show that our method performs well in medical image segmentation tasks.
• Score: 4.777011444412729
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic image segmentation technology is critical to the visual analysis. The autoencoder architecture has satisfying performance in various image segmentation tasks. However, autoencoders based on convolutional neural networks (CNN) seem to encounter a bottleneck in improving the accuracy of semantic segmentation. Increasing the inter-class distance between foreground and background is an inherent characteristic of the segmentation network. However, segmentation networks pay too much attention to the main visual difference between foreground and background, and ignores the detailed edge information, which leads to a reduction in the accuracy of edge segmentation. In this paper, we propose a light-weight end-to-end segmentation framework based on multi-task learning, termed Edge Attention autoencoder Network (EAA-Net), to improve edge segmentation ability. Our approach not only utilizes the segmentation network to obtain inter-class features, but also applies the reconstruction network to extract intra-class features among the foregrounds. We further design a intra-class and inter-class features fusion module -- I2 fusion module. The I2 fusion module is used to merge intra-class and inter-class features, and use a soft attention mechanism to remove invalid background information. Experimental results show that our method performs well in medical image segmentation tasks. EAA-Net is easy to implement and has small calculation cost.

Related papers

• TransResNet: Integrating the Strengths of ViTs and CNNs for High Resolution Medical Image Segmentation via Feature Grafting [6.987177704136503]
  High-resolution images are preferable in medical imaging domain as they significantly improve the diagnostic capability of the underlying method. Most of the existing deep learning-based techniques for medical image segmentation are optimized for input images having small spatial dimensions and perform poorly on high-resolution images. We propose a parallel-in-branch architecture called TransResNet, which incorporates Transformer and CNN in a parallel manner to extract features from multi-resolution images independently.
  arXiv  Detail & Related papers  (2024-10-01T18:22:34Z)
• 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)
• FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation [3.485615723221064]
  FuseNet is a dual-stream framework for self-supervised semantic segmentation. Cross-modal fusion technique extends the principles of CLIP by replacing textual data with augmented images. experiments on skin lesion and lung segmentation datasets demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2023-11-22T00:03:16Z)
• SegNetr: Rethinking the local-global interactions and skip connections in U-shaped networks [1.121518046252855]
  U-shaped networks have dominated the field of medical image segmentation due to their simple and easily tuned structure. We introduce a novel SegNetr block that can perform local-global interactions dynamically at any stage and with only linear complexity. We validate the effectiveness of SegNetr on four mainstream medical image segmentation datasets, with 59% and 76% fewer parameters and GFLOPs than vanilla U-Net.
  arXiv  Detail & Related papers  (2023-07-06T12:39:06Z)
• 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)
• DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation [78.30720731968135]
  Unsupervised domain adaptation in semantic segmentation has been raised to alleviate the reliance on expensive pixel-wise annotations. We propose DecoupleNet that alleviates source domain overfitting and enables the final model to focus more on the segmentation task. We also put forward Self-Discrimination (SD) and introduce an auxiliary classifier to learn more discriminative target domain features with pseudo labels.
  arXiv  Detail & Related papers  (2022-07-20T15:47:34Z)
• 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)
• AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation [86.44683367028914]
  Aerial imagery segmentation has some unique challenges, the most critical one among which lies in foreground-background imbalance. We propose Adaptive Focus Framework (AF$), which adopts a hierarchical segmentation procedure and focuses on adaptively utilizing multi-scale representations. AF$ has significantly improved the accuracy on three widely used aerial benchmarks, as fast as the mainstream method.
  arXiv  Detail & Related papers  (2022-02-18T10:14:45Z)
• Boundary-Aware Segmentation Network for Mobile and Web Applications [60.815545591314915]
  Boundary-Aware Network (BASNet) is integrated with a predict-refine architecture and a hybrid loss for highly accurate image segmentation. BASNet runs at over 70 fps on a single GPU which benefits many potential real applications. Based on BASNet, we further developed two (close to) commercial applications: AR COPY & PASTE, in which BASNet is augmented reality for "COPY" and "PASTING" real-world objects, and OBJECT CUT, which is a web-based tool for automatic object background removal.
  arXiv  Detail & Related papers  (2021-01-12T19:20:26Z)
• Boundary-aware Context Neural Network for Medical Image Segmentation [15.585851505721433]
  Medical image segmentation can provide reliable basis for further clinical analysis and disease diagnosis. Most existing CNNs-based methods produce unsatisfactory segmentation mask without accurate object boundaries. In this paper, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation.
  arXiv  Detail & Related papers  (2020-05-03T02:35:49Z)
• CRNet: Cross-Reference Networks for Few-Shot Segmentation [59.85183776573642]
  Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images. With a cross-reference mechanism, our network can better find the co-occurrent objects in the two images. Experiments on the PASCAL VOC 2012 dataset show that our network achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-03-24T04:55:43Z)

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.