ASDNet: Attention Based Semi-supervised Deep Networks for Medical Image Segmentation

Nie, Dong; Gao, Yaozong; Wang, Li; Shen, Dinggang

doi:10.1007/978-3-030-00937-3_43

Cited by 246 publications

(183 citation statements)

References 9 publications

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“…Min et al (2018) propose a two stream network with independent weights whose concord determine the quality of segmentation mask. Nie et al (2018) propose a segmentation network with adversarial loss where the job of the discriminator network is to identify the reliable annotated regions from noisy annotations. Readers can refer to Section 4.4.2 for more detailed discussion of these approaches.…”

Section: Robust Loss With Iterative Mask Refinementmentioning

confidence: 99%

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Tajbakhsh¹,

Jeyaseelan²,

Li³

et al. 2020

Medical Image Analysis

724

358

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The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. Despite the new performance highs, the recent advanced segmentation models still require large, representative, and high quality annotated datasets. However, rarely do we have a perfect training dataset, particularly in the field of medical imaging, where data and annotations are both expensive to acquire. Recently, a large body of research has studied the problem of medical image segmentation with imperfect datasets, tackling two major dataset limitations: scarce annotations where only limited annotated data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations. In this article, we provide a detailed review of the solutions above, summarizing both the technical novelties and empirical results. We further compare the benefits and requirements of the surveyed methodologies and provide our recommended solutions to the problems of scarce and weak annotations. We hope this review increases the community awareness of the techniques to handle imperfect datasets.

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Section: Robust Loss With Iterative Mask Refinementmentioning

confidence: 99%

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Tajbakhsh¹,

Jeyaseelan²,

Li³

et al. 2020

Medical Image Analysis

724

358

View full text Add to dashboard Cite

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“…Even though attention mechanisms are becoming popular on many vision problems, the literature on medical image segmentation with attention remains scarce, with simple attention modules [23], [24], [25], [26]. Wang et .al [23] employed attention modules at multiple resolutions to combine local deep attention features (DAF) with global context for prostate segmentation on Ultrasound images.…”

Section: Medical Image Segmentation With Deep Attentionmentioning

confidence: 99%

Multi-Scale Self-Guided Attention for Medical Image Segmentation

Sinha

Dolz

2021

IEEE J. Biomed. Health Inform.

442

232

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Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation, standard models still have some drawbacks. First, the use of multi-scale approaches, i.e., encoder-decoder architectures, leads to a redundant use of information, where similar low-level features are extracted multiple times at multiple scales. Second, long-range feature dependencies are not efficiently modeled, resulting in nonoptimal discriminative feature representations associated with each semantic class. In this paper we attempt to overcome these limitations with the proposed architecture, by capturing richer contextual dependencies based on the use of guided self-attention mechanisms. This approach is able to integrate local features with their corresponding global dependencies, as well as highlight interdependent channel maps in an adaptive manner. Further, the additional loss between different modules guides the attention mechanisms to neglect irrelevant information and focus on more discriminant regions of the image by emphasizing relevant feature associations. We evaluate the proposed model in the context of abdominal organ segmentation on magnetic resonance imaging (MRI). A series of ablation experiments support the importance of these attention modules in the proposed architecture. In addition, compared to other state-of-the-art segmentation networks our model yields better segmentation performance, increasing the accuracy of the predictions while reducing the standard deviation. This demonstrates the efficiency of our approach to generate precise and reliable automatic segmentations of medical images. Our code and the trained model are made publicly available at: https://github.com/sinAshish/Multi-Scale-Attention

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“…Comparison with Other Semi-supervised Methods. We implemented several state-of-the-art semi-supervised segmentation methods for comparison, including self-training based method [1], deep adversarial network (DAN) [18], adversarial learning based semi-supervised method (ASDNet) [12], and Π-Model based method (TCSE) [10]. Note that we used the same network backbone (Bayesian V-Net) in these methods for fair comparison.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Zhang et al [18] designed a deep adversarial network to use the unannotated images by encouraging the segmentation of unannotated images to be similar to those of the annotated ones. Another approach [12] utilized an adversarial network to select the trustworthy regions of unlabeled data to train the segmentation network. With the promising results achieved by self-ensembling methods [9,14] on semi-supervised natural image classification, Li et al [10] extended the Π-model [9] with transformation consistent for semi-supervised skin lesion segmentation.…”

mentioning

confidence: 99%

Uncertainty-Aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

Wang

et al. 2019

Lecture Notes in Computer Science

617

535

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Training deep convolutional neural networks usually requires a large amount of labeled data. However, it is expensive and timeconsuming to annotate data for medical image segmentation tasks. In this paper, we present a novel uncertainty-aware semi-supervised framework for left atrium segmentation from 3D MR images. Our framework can effectively leverage the unlabeled data by encouraging consistent predictions of the same input under different perturbations. Concretely, the framework consists of a student model and a teacher model, and the student model learns from the teacher model by minimizing a segmentation loss and a consistency loss with respect to the targets of the teacher model. We design a novel uncertainty-aware scheme to enable the student model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information. Experiments show that our method achieves high performance gains by incorporating the unlabeled data. Our method outperforms the state-of-the-art semi-supervised methods, demonstrating the potential of our framework for the challenging semi-supervised problems 3 .

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ASDNet: Attention Based Semi-supervised Deep Networks for Medical Image Segmentation

Cited by 246 publications

References 9 publications

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Multi-Scale Self-Guided Attention for Medical Image Segmentation

Uncertainty-Aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

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