Semi-Supervised Semantic Segmentation With Cross-Consistency Training

Ouali, Yassine; Hudelot, Céline; Tami, Myriam

doi:10.1109/cvpr42600.2020.01269

Cited by 695 publications

(476 citation statements)

References 35 publications

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“…We compare with two state-of-the-art consistency-related methods whose codes are publicly available [32], [46], achieving 57.22 ± 21.68 (89.53, 64.22) and 51.21 ± 24.11 (91.86, 55.24) in terms of DSC for PDAC segmentation, respectively. To run these methods, we use the same training models (backbone: DenseUNet [47] for [32] and ResNet-50 [48] for [46]) and parameters as reported in [32] and [46]. Models are trained and tested from multiple planes separately in a sliceby-slice manner.…”

Section: Comparison Between Iag-net and Other Methodsmentioning

confidence: 99%

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Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction

Wang

Tang

Zhou

et al. 2021

IEEE Trans. Med. Imaging

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Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the United States. Predicting tumors like PDACs (including both classification and segmentation) from medical images by deep learning is becoming a growing trend, but usually a large number of annotated data are required for training, which is very labor-intensive and time-consuming. In this paper, we consider a partially supervised setting, where cheap imagelevel annotations are provided for all the training data, and the costly per-voxel annotations are only available for a subset of them. We propose an Inductive Attention Guidance Network (IAG-Net) to jointly learn a global image-level classifier for normal/PDAC classification and a local voxellevel classifier for semi-supervised PDAC segmentation. We instantiate both the global and the local classifiers by multiple instance learning (MIL), where the attention guidance, indicating roughly where the PDAC regions are, is the key to bridging them: For global MIL based normal/PDAC classification, attention serves as a weight for each instance (voxel) during MIL pooling, which eliminates the distraction from the background; For local MIL based semisupervised PDAC segmentation, the attention guidance is inductive, which not only provides bag-level pseudo-labels to training data without per-voxel annotations for MIL training, but also acts as a proxy of an instance-level classifier. Experimental results show that our IAG-Net boosts PDAC segmentation accuracy by more than 5% compared with the state-of-the-arts.

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Section: Comparison Between Iag-net and Other Methodsmentioning

confidence: 99%

“…Note that, [46] uses ResNet-50 as the backbone network, which is a stronger backbone than VGG-Net, as shown in [48]. We also test our IAG-Net with ResNet-50, and achieve 56.…”

Section: Comparison Between Iag-net and Other Methodsmentioning

confidence: 99%

Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction

Wang

Tang

Zhou

et al. 2021

IEEE Trans. Med. Imaging

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“…The first one is unsupervised or self-supervised pretraining, followed by fine-tuning on a small set of labeled data. The second paradigm is to jointly use the labeled data and unlabeled data through pseudo labeling [4] or consistency regularization [5][6][7][8]. Since there is an obvious gap between the objectives of the unsupervised pretraining and the downstream segmentation, the effect of unsupervised pretraining is not always significant.…”

Section: Introductionmentioning

confidence: 99%

“…Consistency regularization encourages the segmentation prediction to be consistent on the unlabeled examples under different data perturbations or among different models. We follow the studies in [ 6 , 9 , 10 ] and enforce consistency among different models' predictions. Both strong and weak perturbations are applied.…”

Section: Introductionmentioning

confidence: 99%

“…To be specific, our model, as shown in Figure 1 , consists of one shared encoder and three separate decoders: one decoder for supervised learning and the other two decoders for unsupervised consistency learning. Following a similar strategy as in [ 6 ], different perturbations are injected on the two unsupervised decoders. We enforce consistency between the predictions of the supervised decoder and unsupervised decoders to make the learned model less sensitive to the extra perturbation.…”

Section: Introductionmentioning

confidence: 99%

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LCC-Net: A Lightweight Cross-Consistency Network for Semisupervised Cardiac MR Image Segmentation

Song

2021

Computational and Mathematical Methods in Medicine

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Semantic segmentation plays a crucial role in cardiac magnetic resonance (MR) image analysis. Although supervised deep learning methods have made significant performance improvements, they highly rely on a large amount of pixel-wise annotated data, which are often unavailable in clinical practices. Besides, top-performing methods usually have a vast number of parameters, which result in high computation complexity for model training and testing. This study addresses cardiac image segmentation in scenarios where few labeled data are available with a lightweight cross-consistency network named LCC-Net. Specifically, to reduce the risk of overfitting on small labeled datasets, we substitute computationally intensive standard convolutions with a lightweight module. To leverage plenty of unlabeled data, we introduce extreme consistency learning, which enforces equivariant constraints on the predictions of different perturbed versions of the input image. Cutting and mixing different training images, as an extreme perturbation on both the labeled and unlabeled data, are utilized to enhance the robust representation learning. Extensive comparisons demonstrate that the proposed model shows promising performance with high annotation- and computation-efficiency. With only two annotated subjects for model training, the LCC-Net obtains a performance gain of 14.4% in the mean Dice over the baseline U-Net trained from scratch.

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Dual‐branch Transformer for semi‐supervised medical image segmentation

Huang,

Zhu,

Shao

et al. 2024

J Applied Clin Med Phys

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PurposeIn recent years, the use of deep learning for medical image segmentation has become a popular trend, but its development also faces some challenges. Firstly, due to the specialized nature of medical data, precise annotation is time‐consuming and labor‐intensive. Training neural networks effectively with limited labeled data is a significant challenge in medical image analysis. Secondly, convolutional neural networks commonly used for medical image segmentation research often focus on local features in images. However, the recognition of complex anatomical structures or irregular lesions often requires the assistance of both local and global information, which has led to a bottleneck in its development. Addressing these two issues, in this paper, we propose a novel network architecture.MethodsWe integrate a shift window mechanism to learn more comprehensive semantic information and employ a semi‐supervised learning strategy by incorporating a flexible amount of unlabeled data. Specifically, a typical U‐shaped encoder‐decoder structure is applied to obtain rich feature maps. Each encoder is designed as a dual‐branch structure, containing Swin modules equipped with windows of different size to capture features of multiple scales. To effectively utilize unlabeled data, a level set function is introduced to establish consistency between the function regression and pixel classification.ResultsWe conducted experiments on the COVID‐19 CT dataset and DRIVE dataset and compared our approach with various semi‐supervised and fully supervised learning models. On the COVID‐19 CT dataset, we achieved a segmentation accuracy of up to 74.56%. Our segmentation accuracy on the DRIVE dataset was 79.79%.ConclusionsThe results demonstrate the outstanding performance of our method on several commonly used evaluation metrics. The high segmentation accuracy of our model demonstrates that utilizing Swin modules with different window sizes can enhance the feature extraction capability of the model, and the level set function can enable semi‐supervised models to more effectively utilize unlabeled data. This provides meaningful insights for the application of deep learning in medical image segmentation. Our code will be released once the manuscript is accepted for publication.

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Semi-Supervised Semantic Segmentation With Cross-Consistency Training

Cited by 695 publications

References 35 publications

Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction

Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction

LCC-Net: A Lightweight Cross-Consistency Network for Semisupervised Cardiac MR Image Segmentation

Dual‐branch Transformer for semi‐supervised medical image segmentation

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