CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

• URL: http://arxiv.org/abs/2009.10549v2
• Date: Wed, 23 Sep 2020 01:03:45 GMT
• Title: CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation
• Authors: Ran Gu, Guotai Wang, Tao Song, Rui Huang, Michael Aertsen, Jan Deprest, S\'ebastien Ourselin, Tom Vercauteren, Shaoting Zhang
• Abstract summary: We make extensive use of multiple attentions in a CNN architecture and propose a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation. Our proposed CA-Net significantly improved the average segmentation Dice score from 87.77% to 92.08% for skin lesion. It reduced the model size to around 15 times smaller with close or even better accuracy compared with state-of-the-art DeepLabv3+.
• Score: 20.848367370280695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they are still challenged by complicated conditions where the segmentation target has large variations of position, shape and scale, and existing CNNs have a poor explainability that limits their application to clinical decisions. In this work, we make extensive use of multiple attentions in a CNN architecture and propose a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation that is aware of the most important spatial positions, channels and scales at the same time. In particular, we first propose a joint spatial attention module to make the network focus more on the foreground region. Then, a novel channel attention module is proposed to adaptively recalibrate channel-wise feature responses and highlight the most relevant feature channels. Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object. Extensive experiments on skin lesion segmentation from ISIC 2018 and multi-class segmentation of fetal MRI found that our proposed CA-Net significantly improved the average segmentation Dice score from 87.77% to 92.08% for skin lesion, 84.79% to 87.08% for the placenta and 93.20% to 95.88% for the fetal brain respectively compared with U-Net. It reduced the model size to around 15 times smaller with close or even better accuracy compared with state-of-the-art DeepLabv3+. In addition, it has a much higher explainability than existing networks by visualizing the attention weight maps. Our code is available at https://github.com/HiLab-git/CA-Net

Related papers

• MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation [36.837642256513426]
  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.
  arXiv  Detail & Related papers  (2024-05-07T07:10:44Z)
• AttResDU-Net: Medical Image Segmentation Using Attention-based Residual Double U-Net [0.0]
  This paper proposes an attention-based residual Double U-Net architecture (AttResDU-Net) that improves on the existing medical image segmentation networks. We conducted experiments on three datasets: CVC Clinic-DB, ISIC 2018, and the 2018 Data Science Bowl datasets and achieved Dice Coefficient scores of 94.35%, 91.68%, and 92.45% respectively.
  arXiv  Detail & Related papers  (2023-06-25T14:28:08Z)
• 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)
• CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects [0.0]
  This paper proposes a Context Axial Reverse Attention Network (CaraNet) to improve the segmentation performance on small objects. Our CaraNet achieves the top-rank mean Dice segmentation accuracy, and results show a distinct advantage of CaraNet in the segmentation of small medical objects.
  arXiv  Detail & Related papers  (2023-01-31T02:12:33Z)
• 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)
• CNN-based fully automatic wrist cartilage volume quantification in MR Image [55.41644538483948]
  The U-net convolutional neural network with additional attention layers provides the best wrist cartilage segmentation performance. The error of cartilage volume measurement should be assessed independently using a non-MRI method.
  arXiv  Detail & Related papers  (2022-06-22T14:19:06Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects [0.0]
  This paper proposes a Context Axial Reserve Attention Network (CaraNet) to improve the segmentation performance on small objects. Our CaraNet achieves the top-rank mean Dice segmentation accuracy, but also shows a distinct advantage in segmentation of small medical objects.
  arXiv  Detail & Related papers  (2021-08-16T22:48:47Z)
• The Mind's Eye: Visualizing Class-Agnostic Features of CNNs [92.39082696657874]
  We propose an approach to visually interpret CNN features given a set of images by creating corresponding images that depict the most informative features of a specific layer. Our method uses a dual-objective activation and distance loss, without requiring a generator network nor modifications to the original model.
  arXiv  Detail & Related papers  (2021-01-29T07:46:39Z)
• DONet: Dual Objective Networks for Skin Lesion Segmentation [77.9806410198298]
  We propose a simple yet effective framework, named Dual Objective Networks (DONet), to improve the skin lesion segmentation. Our DONet adopts two symmetric decoders to produce different predictions for approaching different objectives. To address the challenge of large variety of lesion scales and shapes in dermoscopic images, we additionally propose a recurrent context encoding module (RCEM)
  arXiv  Detail & Related papers  (2020-08-19T06:02:46Z)
• An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism [4.4259821861544]
  coronavirus disease (COVID-19) pandemic has led to a devastating effect on the global public health. It is of great importance to rapidly and accurately segment COVID-19 from CT to help diagnostic and patient monitoring. We propose a U-Net based segmentation network using attention mechanism.
  arXiv  Detail & Related papers  (2020-04-14T17:21:11Z)

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.