Cost Sensitive Semi-Supervised Canonical Correlation Analysis for Multi-view Dimensionality Reduction

Wan, Jianwu; Wang, Hongyuan; Yang, Ming

doi:10.1007/s11063-016-9532-z

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…And to our best knowledge, the performance of the CNN model was always relevant to the size of the training set. So in the future, some semi-supervised learning (SSL) (Wan et al 2016) or even unsupervised learning (Guo et al 2023) strategies will be attempted into the CAD method on the purpose of improving the behavior of our CAD method without increasing the number of labels.…”

Section: Discussionmentioning

confidence: 99%

Automatic ultrasound diagnosis of thyroid nodules: a combination of deep learning and KWAK TI-RADS

Zhang,

Wang,

Zhao

et al. 2023

Phys. Med. Biol.

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Objective:There has been a considerable amount of Computer-Aided Diagnosis (CAD) methods highlighted in the field of ultrasonic examination (USE) of thyroid nodules. However, few researches focused on the automatic risk classification, which was the basis for determining whether Fine Needle Aspiration (FNA) was needed. The aim of this work was to implement automatic risk level assessment of thyroid nodules.Approach:Firstly, 1862 cases of thyroid nodules with the results of USE and FNA were collected as the dataset. Then, an improved U-Net++ model was utilized for segmenting thyroid nodules in ultrasound images automatically. Finally, the segmentation result was imported into a multi-task convolutional neural network (MT-CNN), the design of which was based on the clinical guideline called KWAK TI-RADS. Apart from the category of benign and malignant, the MT-CNN also exported the classification result of four malignant features, Solid Component (SC), Hypoechogenicity or Marked Hypoechogenicity (HMH), Microlobulated or Irregular Margin (MIM), Microcalcification (MC), which were used for counting the risk level in KWAK TI-RADS.Main results:The performance of the improved U-Net++ was evaluated on our test set, including 302 cases. The Dice coefficient and Intersection over Union (IoU) reached 0.899, 0.816, respectively. The classification accuracy rates of SC, HMH, MIM, MC, were 94.5%, 92.8%, 86.1%, 88.9%, while the false positive (FP) rate was 6.0%, 5.6%, 10.6%, 12.9% separately. As for the category of benign and malignant, the precision and recall rates were 93.7% and 94.4%.Significance:The proposed CAD method showed favourable performance in the diagnosis of thyroid nodules. Compared with other methods, it could provide reports closer to clinical practice for doctors.

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Section: Discussionmentioning

confidence: 99%

Automatic ultrasound diagnosis of thyroid nodules: a combination of deep learning and KWAK TI-RADS

Zhang,

Wang,

Zhao

et al. 2023

Phys. Med. Biol.

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“…Now turn to the solution of problem (15). Similarly to that of MCCA, it can also be transformed into a generalized eigenvalue decomposition problem      0C (12) • • •C (1m) C (21) 0 (2) . .…”

Section: B Lrmcca Solutionmentioning

confidence: 99%

“…We hope to find m suitable projection matrix P (i) ∈ R D i ×d m i=1 (d < D i ) to reduce the dimensions of multi-view data into a lower common dimension by P (i)T X (i) m i=1 . Among recent methods [16]- [20] the most representative is to apply the canonical correlation analysis (CCA) [16], [21], [22] to two views of data. The idea of CCA is to extract the canonical variables P (1)T X (1) and P (2)T X (2) from the mean-normalized two-view data X (1) = x (1) 1 , .…”

Section: Introductionmentioning

confidence: 99%

Multiset Canonical Correlations Analysis With Global Structure Preservation

Zhang

Jing

2020

IEEE Access

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This paper considers unsupervised dimensionality reduction of multi-view data, where locality preserving canonical correlation analysis and a new locality-preserving canonical correlation analysis are two typical effective methods. However, they ignore the global structure while considering the local structure of data, and are sensitive to noises because of the relationship of neighbors based on the Euclidean distance. In this paper, we propose a novel multi-view dimensionality reduction method: multiset canonical correlations analysis based on low-rank representation. Our model introduces the cross-view similarity matrix to consider the correlation of all different points in cross views, which makes it not only preserve the local structure but also the global structure of data. And the cross-view similarity matrix is constructed by using low-rank representation, which can make the model more robust. In addition, a parameter β is introduced to adjust the importance of the correlation of different sample points, enhancing the generalization ability for different datasets. Experiments on four multi-view datasets show our proposed method has better performance than the related methods.

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