IEEE journal of biomedical and health informatics
31581101
Single-cell RNA-Sequencing (scRNA-Seq), an advanced sequencing technique, enables biomedical researchers to characterize cell-specific gene expression profiles. Although studies have adapted machine learning algorithms to cluster different cell popul...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
33018353
We present a new LSTM (P-LSTM: Progressive LSTM) network, aiming to predict morphology and states of cell colonies from time-lapse microscopy images. Apparent short-term changes occur in some types of time-lapse cell images. Therefore, long-term-memo...
Machine learning and topological analysis methods are becoming increasingly used on various large-scale omics datasets. Modern high dimensional flow cytometry data sets share many features with other omics datasets like genomics and proteomics. For e...
Stem cell-based products have clinical and industrial applications. Thus, there is a need to develop quality control methods to standardize stem cell manufacturing. Here, we report a deep learning-based automated cell tracking (DeepACT) technology fo...
Machine learning techniques are increasingly becoming incorporated into biological research workflows in a variety of disciplines, most notably cancer research and drug discovery. Efforts in stem cell research comparatively lag behind. We detail key ...
Advanced high-grade serous ovarian cancer continues to be a therapeutic challenge for those affected using the current therapeutic interventions. There is an increasing interest in personalized cancer immunotherapy using activated natural killer (NK)...
Frequently, neural network training involving biological images suffers from a lack of data, resulting in inefficient network learning. This issue stems from limitations in terms of time, resources, and difficulty in cellular experimentation and data...
Computational intelligence and neuroscience
35422850
The current work aims to strengthen the research of segmentation, detection, and tracking methods of stem cell image in the fields of regenerative medicine and tissue damage restoration. Firstly, based on the relevant theories of stem cell image segm...
U-Net is the most cited and widely-used deep learning model for biomedical image segmentation. In this paper, we propose a new enhanced version of a ubiquitous U-Net architecture, which improves upon the original one in terms of generalization capabi...