High-Order Attention Networks for Medical Image Segmentation

Ding, Fei; Yang, Gang; Wu, Jun; Ding, Dayong; Xv, Jie; Cheng, Genyang; Li, Xirong

doi:10.1007/978-3-030-59710-8_25

Cited by 20 publications

(7 citation statements)

References 14 publications

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“…Ding et al 77 introduced hierarchical attention networks for medical image segmentation. The approach combined encoder-decoder networks with CNNs to extract feature maps.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation – A Review

Alawad¹,

Aljouie²,

Alamri³

et al. 2022

OPTH

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Background Globally, glaucoma is the second leading cause of blindness. Detecting glaucoma in the early stages is essential to avoid disease complications, which lead to blindness. Thus, computer-aided diagnosis systems are powerful tools to overcome the shortage of glaucoma screening programs. Methods A systematic search of public databases, including PubMed, Google Scholar, and other sources, was performed to identify relevant studies to overview the publicly available fundus image datasets used to train, validate, and test machine learning and deep learning methods. Additionally, existing machine learning and deep learning methods for optic cup and disc segmentation were surveyed and critically reviewed. Results Eight fundus images datasets were publicly available with 15,445 images labeled with glaucoma or non-glaucoma, and manually annotated optic disc and cup boundaries were found. Five metrics were identified for evaluating the developed models. Finally, three main deep learning architectural designs were commonly used for optic disc and optic cup segmentation. Conclusion We provided future research directions to formulate robust optic cup and disc segmentation systems. Deep learning can be utilized in clinical settings for this task. However, many challenges need to be addressed before using this strategy in clinical trials. Finally, two deep learning architectural designs have been widely adopted, such as U-net and its variants.

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“…Ding et al 77 introduced hierarchical attention networks for medical image segmentation. The approach combined encoder-decoder networks with CNNs to extract feature maps.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation – A Review

Alawad¹,

Aljouie²,

Alamri³

et al. 2022

OPTH

View full text Add to dashboard Cite

show abstract

“…With the development of deep learning research, a large number of semantic segmentation methods have been proposed for skin lesion segmentation. Recently, the methods mainly focus on network structure, 16,17 sampling mechanisms, 18,19 and data augmentation, 20 whereas some mainly use additional constraints. 21,22 U-Net 23 is a traditional network in the tasks of medical image segmentation.…”

Section: Skin Lesion Segmentationmentioning

confidence: 99%

Confidence-aware cross-supervised model for semi-supervised skin lesion segmentation

Shen

Sun

et al. 2023

J. Electron. Imag.

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.A long-standing problem in melanoma segmentation is that the pixel-level skin lesion label must be manually labeled by experts. Semi-supervised learning is considered a promising way to solve this problem. However, the pseudo labels obtained in the training phase are not accurate and will introduce wrong guidance to the model’s training. The segmentation of the unlabeled data after filtering by the confidence map can be a reliable pseudo label in training processing. Thus, a confidence-aware cross-supervised network is proposed for skin lesion segmentation, using captured confidence to make the training process more reliable. Our method is composed of two parallel and independently initialized segmentation networks. The output of one segmentation branch filters the low-confidence area by the confidence map of the same network and is used as a pseudo label to supervise another branch. Furthermore, a consistency loss is designed to calculate cross-entropy between the pseudo label and prediction segmentation to make full use of the unlabeled data. We also propose a simple yet effective attention module to capture the feature from the target area. This approach effectively mines the semantic information of the input image. To validate the effectiveness of our method, we conduct experiments on the ISIC 2017 dataset and achieve state-of-the-art results.

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“…To encode global information and contextual representations, [29], Ding et al proposed Highorder Attention (HA) with adaptive receptive fields and dynamic weights. HA mainly constructs a feature map for each pixel, including the relationships to other pixels.…”

Section: High-order Attentionmentioning

confidence: 99%

Visual Attention Methods in Deep Learning: An In-Depth Survey

Hassanin¹,

Anwar²,

Radwan³

et al. 2022

Preprint

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Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more on the essential aspects of data. Deep learning has employed attention to boost performance for many applications. Interestingly, the same attention design can suit processing different data modalities and can easily be incorporated into large networks. Furthermore, multiple complementary attention mechanisms can be incorporated in one network. Hence, attention techniques have become extremely attractive. However, the literature lacks a comprehensive survey specific to attention techniques to guide researchers in employing attention in their deep models. Note that, besides being demanding in terms of training data and computational resources, transformers only cover a single category in self-attention out of the many categories available. We fill this gap and provide an in-depth survey of 50 attention techniques categorizing them by their most prominent features. We initiate our discussion by introducing the fundamental concepts behind the success of attention mechanism. Next, we furnish some essentials such as the strengths and limitations of each attention category, describe their fundamental building blocks, basic formulations with primary usage, and applications specifically for computer vision. We also discuss the challenges and open questions related to attention mechanism in general. Finally, we recommend possible future research directions for deep attention.

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High-Order Attention Networks for Medical Image Segmentation

Cited by 20 publications

References 14 publications

Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation – A Review

Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation – A Review

Confidence-aware cross-supervised model for semi-supervised skin lesion segmentation

Visual Attention Methods in Deep Learning: An In-Depth Survey

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