AIMC Topic: Algorithms

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Learning Transferable Driven and Drone Assisted Sustainable and Robust Regional Disease Surveillance for Smart Healthcare.

IEEE/ACM transactions on computational biology and bioinformatics
Smart healthcare has been applied in many fields such as disease surveillance and telemedicine, etc. However, there are some challenges for device deployment, data collection and guarantee of stainability in regional disease surveillance. First, it i...

A Transfer Learning Based Super-Resolution Microscopy for Biopsy Slice Images: The Joint Methods Perspective.

IEEE/ACM transactions on computational biology and bioinformatics
Higher-resolution biopsy slice images reveal many details, which are widely used in medical practice. However, taking high-resolution slice images is more costly than taking low-resolution ones. In this paper, we propose a joint framework containing ...

ResNet-SCDA-50 for Breast Abnormality Classification.

IEEE/ACM transactions on computational biology and bioinformatics
(Aim) Breast cancer is the most common cancer in women and the second most common cancer worldwide. With the rapid advancement of deep learning, the early stages of breast cancer development can be accurately detected by radiologists with the help of...

Cross-Domain Classification Model With Knowledge Utilization Maximization for Recognition of Epileptic EEG Signals.

IEEE/ACM transactions on computational biology and bioinformatics
Conventional classification models for epileptic EEG signal recognition need sufficient labeled samples as training dataset. In addition, when training and testing EEG signal samples are collected from different distributions, for example, due to dif...

A Novel Negative-Transfer-Resistant Fuzzy Clustering Model With a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation.

IEEE/ACM transactions on computational biology and bioinformatics
Traditional clustering algorithms for medical image segmentation can only achieve satisfactory clustering performance under relatively ideal conditions, in which there is adequate data from the same distribution, and the data is rarely disturbed by n...

A Method for Generating Synthetic Electronic Medical Record Text.

IEEE/ACM transactions on computational biology and bioinformatics
Machine learning (ML) and Natural Language Processing (NLP) have achieved remarkable success in many fields and have brought new opportunities and high expectation in the analyses of medical data, of which the most common type is the massive free-tex...

An Ontology-Independent Representation Learning for Similar Disease Detection Based on Multi-Layer Similarity Network.

IEEE/ACM transactions on computational biology and bioinformatics
To identify similar diseases has significant implications for revealing the etiology and pathogenesis of diseases and further research in the domain of biomedicine. Currently, most methods for the measurement of disease similarity utilize either asso...

Protein2Vec: Aligning Multiple PPI Networks with Representation Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Research of Protein-Protein Interaction (PPI) Network Alignment is playing an important role in understanding the crucial underlying biological knowledge such as functionally homologous proteins and conserved evolutionary pathways across different sp...

FlyIT: Drosophila Embryogenesis Image Annotation based on Image Tiling and Convolutional Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
With the rise of image-based transcriptomics, spatial gene expression data has become increasingly important for understanding gene regulations from the tissue level down to the cell level. Especially, the gene expression images of Drosophila embryos...

Cross-species Data Classification by Domain Adaptation via Discriminative Heterogeneous Maximum Mean Discrepancy.

IEEE/ACM transactions on computational biology and bioinformatics
Cross-species or Cross-platform data classification is a challenging problem in the field of bioinformatics, which aims to classify data samples in one species/platform by using labeled data samples in another species/platform. Traditional classifica...