CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation
Ran Gu,Guotai Wang,Tao Song,Rui Huang,Michael Aertsen,Jan Deprest,Sebastien Ourselin,Tom Vercauteren,Shaoting Zhang +8 more
TLDR
CA-Net as mentioned in this paper proposes a joint spatial attention module to make the network focus more on the foreground region and a novel channel attention module is proposed to adaptively recalibrate channel-wise feature responses and highlight the most relevant feature channels.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 .read more
Citations
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Explainable artificial intelligence: a comprehensive review
TL;DR: A review of explainable artificial intelligence (XAI) can be found in this article, where the authors analyze and review various XAI methods, which are grouped into (i) pre-modeling explainability, (ii) interpretable model, and (iii) post-model explainability.
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Ms RED: A novel multi-scale residual encoding and decoding network for skin lesion segmentation
TL;DR: Li et al. as mentioned in this paper proposed a multi-scale residual encoding and decoding network (Ms RED) for skin lesion segmentation, which is able to accurately and reliably segment a variety of lesions with efficiency.
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ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions
Adriano Lucieri,Muhammad Naseer Bajwa,Stephan Alexander Braun,Muhammad Imran Malik,Andreas Dengel,Sheraz Ahmed +5 more
TL;DR: In this article , the authors present ExAID (Explainable AI for Dermatology), a novel XAI framework for biomedical image analysis that provides multi-modal concept-based explanations, consisting of easy-tounderstand textual explanations and visual maps, to justify the predictions.
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MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation
TL;DR: In this paper, a hierarchical block is introduced between the encoder-decoder for acquiring and merging features to extract multi-scale information in the proposed architecture, which achieves state-of-the-art performance on four challenging MICCAI datasets.
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UMAG-Net: A New Unsupervised Multiattention-Guided Network for Hyperspectral and Multispectral Image Fusion
TL;DR: In this article, an unsupervised multi-attention-guided network named UMAG-Net was proposed to fuse a low-resolution hyperspectral image (HSI) with a high-resolution (HR) multispectral images (MSI) of the same scene.
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