Deep cross-view co-regularized representation learning for glioma subtype identification

Ning, Zhenyuan; Tu, Chao; Di, Xiaohui; Feng, Qianjin

doi:10.1016/j.media.2021.102160

Cited by 9 publications

(5 citation statements)

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“…On the other hand, since the PEDF landscape represents the similarity between pixel points, it accentuates the subgraphs in grid space that have attributes of high-intensity or dense edge connections, as indicated by the red arrow in the inset. Recent research also validates that multi-view learning could reinforce the complementary information of different views, and the feature-searching in a specific latent space would raise the accuracy of object recognition 3 , 20 . Thus, this work supports the mechanism of our proposed AutoEncoder-assisted module and the 2D-FFT operations in the latent space.…”

Section: Methodsmentioning

confidence: 68%

“…Sophisticated deep neural networks on lesion recognition, tracking, and segmentation of various medical images have also further promoted the evolution of clinical investigations. Glioma modality identification has attracted significant attention from scientists and engineers and orientated the mainstream development of tumor image segmentation due to its high aggressiveness 1 , 2 and infiltrative 3 properties. Thus, image-vision-based neural networks have reached fruitful achievements on high-dimensional and multi-channel glioma image recognition and segmentation tasks 3 , 4 .…”

Section: Introductionmentioning

confidence: 99%

“…Glioma modality identification has attracted significant attention from scientists and engineers and orientated the mainstream development of tumor image segmentation due to its high aggressiveness 1 , 2 and infiltrative 3 properties. Thus, image-vision-based neural networks have reached fruitful achievements on high-dimensional and multi-channel glioma image recognition and segmentation tasks 3 , 4 . However, neural networks are pushed to incorporate more complex structures and require large datasets and time costs to train the model effectively.…”

Section: Introductionmentioning

confidence: 99%

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Feature-aware unsupervised lesion segmentation for brain tumor images using fast data density functional transform

Huang,

Chen,

Kao

et al. 2023

Sci Rep

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We demonstrate that isomorphically mapping gray-level medical image matrices onto energy spaces underlying the framework of fast data density functional transform (fDDFT) can achieve the unsupervised recognition of lesion morphology. By introducing the architecture of geometric deep learning and metrics of graph neural networks, gridized density functionals of the fDDFT establish an unsupervised feature-aware mechanism with global convolutional kernels to extract the most likely lesion boundaries and produce lesion segmentation. An AutoEncoder-assisted module reduces the computational complexity from $$\mathcal{O}\left({N}^{3}\right)$$ O N 3 to $$\mathcal{O}\left(N\mathrm{log}N\right)$$ O N log N , thus efficiently speeding up global convolutional operations. We validate their performance utilizing various open-access datasets and discuss limitations. The inference time of each object in large three-dimensional datasets is 1.76 s on average. The proposed gridized density functionals have activation capability synergized with gradient ascent operations, hence can be modularized and embedded in pipelines of modern deep neural networks. Algorithms of geometric stability and similarity convergence also raise the accuracy of unsupervised recognition and segmentation of lesion images. Their performance achieves the standard requirement for conventional deep neural networks; the median dice score is higher than 0.75. The experiment shows that the synergy of fDDFT and a naïve neural network improves the training and inference time by 58% and 51%, respectively, and the dice score raises to 0.9415. This advantage facilitates fast computational modeling in interdisciplinary applications and clinical investigation.

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Section: Methodsmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature-aware unsupervised lesion segmentation for brain tumor images using fast data density functional transform

Huang,

Chen,

Kao

et al. 2023

Sci Rep

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“…The use of these methods has been proven to be an efficient way to overcome the shortcomings of traditional image analysis on many sub-specialty applications. DL methods in medical image analysis have been applied in MRI tumor grading [8][9][10], thyroid nodule ultrasound classification [11][12][13] and CT pulmonary nodule detection [14][15][16]. However, only a limited number of studies have been performed to analyze the musculoskeletal imaging associated with the lesion.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Osteochondral Lesions of the Talus via Deep Learning

Wang

Li²,

Zhu

et al. 2022

Front. Phys.

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Screening of osteochondral lesions of the talus (OLTs) from MR imags usually requires time and efforts, and in most case lesions with small size are often missed in clinical practice. Thereore, it puts forward higher requirements for a more efficient OLTs screening method. To develop an automatic screening system for osteochondral lesions of the talus (OLTs), we collected 92 MRI images of patients with ankle pain from Qilu Hospital of Shandong University and proposed an AI (artificial intelligence) aided lesion screening system, which is automatic and based on deep learning method. A two-stage detection method based on the cascade R-CNN model was proposed to significantly improve the detection performance by taking advantage of multiple intersection-over-union thresholds. The backbone network was based on ResNet50, which was a state-of-art convolutional neural network model in image classification task. Multiple regression using cascaded detection heads was applied to further improve the detection precision. The mean average precision (mAP) that is adopted as major metrics in the paper and mean average recall (mAR) was selected to evaluate the performance of the model. Our proposed method has an average precision of 0.950, 0.975, and 0.550 for detecting the talus, gaps and lesions, respectively, and the mAP, mAR was 0.825, 0.930. Visualization of our network performance demonstrated the effectiveness of the model, which implied that accurate detection performance on these tasks could be further used in real clinical practice.

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“…Tarvainen and Valpola [36] suggested that consistency regularization guides the two networks of the mean teacher model to adaptively correct their training errors and attain consistent predictions by improving each other feature representations. Consistency regularization has been widely used to learn meaningful feature representations [31,7,26]. For instance, in semi-supervised learning (SSL), consistency regularization based methods have proven useful in improving representation learning by harnessing unlabeled data [6,8,32,24,30,23].…”

Section: Introductionmentioning

confidence: 99%

Representation Learning with Information Theory for COVID-19 Detection

Berenguer¹,

Mukherjee²,

Bossa³

et al. 2022

Preprint

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Successful data representation is a fundamental factor in machine learning based medical imaging analysis. Deep Learning (DL) has taken an essential role in robust representation learning. However, the inability of deep models to generalize to unseen data can quickly overfit intricate patterns. Thereby, we can conveniently implement strategies to aid deep models in discovering useful priors from data to learn their intrinsic properties. Our model, which we call a dual role network (DRN), uses a dependency maximization approach based on Least Squared Mutual Information (LSMI). LSMI leverages dependency measures to ensure representation invariance and local smoothness. While prior works have used information theory dependency measures like mutual information, these are known to be computationally expensive due to the density estimation step. In contrast, our proposed DRN with LSMI formulation does not require the density estimation step and can be used as an alternative to approximate mutual information. Experiments on the CT based COVID-19 Detection and COVID-19 Severity Detection benchmarks of the 2nd COV19D competition [16] demonstrate the effectiveness of our method compared to the baseline method of such competition.

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Deep cross-view co-regularized representation learning for glioma subtype identification

Cited by 9 publications

References 50 publications

Feature-aware unsupervised lesion segmentation for brain tumor images using fast data density functional transform

Feature-aware unsupervised lesion segmentation for brain tumor images using fast data density functional transform

Automatic Detection of Osteochondral Lesions of the Talus via Deep Learning

Representation Learning with Information Theory for COVID-19 Detection

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