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Presents corrections to the paper, Multi-View Feature Transformation Based SVM+ for Computer-Aided Diagnosis of Liver Cancers With Ultrasound Image.
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http://dx.doi.org/10.1109/JBHI.2024.3404528 | DOI Listing |
IEEE Trans Med Imaging
September 2025
Mammography is a primary method for early screening, and developing deep learning-based computer-aided systems is of great significance. However, current deep learning models typically treat each image as an independent entity for diagnosis, rather than integrating images from multiple views to diagnose the patient. These methods do not fully consider and address the complex interactions between different views, resulting in poor diagnostic performance and interpretability.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
Drug-target interaction (DTI) identification is of great significance in drug development in various areas, such as drug repositioning and potential drug side effects. Although a great variety of computational methods have been proposed for DTI prediction, it is still a challenge in the face of sparsely correlated drugs or targets. To address the impact of data sparsity on the model, we propose a multi-view neighborhood-enhanced graph contrastive learning approach (MneGCL), which is based on graph clustering according to the adjacency relationship in various similarity networks between drugs or targets, to fully exploit the information of drugs and targets with few corrections.
View Article and Find Full Text PDFIEEE Trans Biomed Eng
September 2025
Diagnostic ultrasound has long filled a crucial niche in medical imaging thanks to its portability, affordability, and favorable safety profile. Now, multi-view hardware and deep-learning-based image reconstruction algorithms promise to extend this niche to increasingly sophisticated applications, such as volume rendering and long-term organ monitoring. However, progress on these fronts is impeded by the complexities of ultrasound electronics and by the scarcity of high-fidelity radiofrequency data.
View Article and Find Full Text PDFBioinformatics
September 2025
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
Motivation: Therapeutic peptide is an important ingredient in the treatment of various diseases and drug discovery. The toxicity of peptides is one of the major challenges in peptide drug therapy. With the abundance of therapeutic peptides generated in the post-genomics era, it is a challenge to promptly identify toxicity peptides using computational methods.
View Article and Find Full Text PDFIEEE Trans Comput Biol Bioinform
September 2025
Sparse Partial Least Squares (sPLS) is a common dimensionality reduction technique for data fusion, which projects data samples from two views by seeking linear combinations with a small number of variables with the maximum variance. However, sPLS extracts the combinations between two data sets with all data samples so that it cannot detect latent subsets of samples. To extend the application of sPLS by identifying a specific subset of samples and remove outliers, we propose an $\ell _\infty /\ell _{0}$-norm constrained weighted sparse PLS ($\ell _\infty /\ell _{0}$-wsPLS) method for joint sample and feature selection, where the $\ell _\infty /\ell _{0}$-norm constrains are used to select a subset of samples.
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