Frontiers Deep Learning Models For Cancer Stem Cell Detection A 1 introduction. cancer stem cells (cscs), also known as tumor initiating cells (tics), are a subpopulation of tumor cells. ()these cells are thought to persist within tumors as a distinct population, driving tumor initiation, relapse, and metastasis through self renewal and differentiation into multiple cell types, similar to the typical stem cell processes (). This mini review explores the emerging trend of deep learning research in the field of cscs. it introduces diverse convolutional neural network (cnn) based deep learning models for stem cell research and discusses the application of deep learning for csc research. finally, it provides perspectives and limitations in the field of deep learning.
Frontiers Deep Learning Models For Cancer Stem Cell Detection A Finally, it provides perspectives and limitations in the field of deep learning based stem cell research. different types of stem cells. (a) cancer stem cells (cscs) with the ability to form tumors. A mini review explores the emerging trend of deep learning research in the field of cancer stem cells and introduces diverse convolutional neural network (cnn) based deep learning models for stem cell research and discusses the application ofdeep learning for csc research. cancer stem cells (cscs), also known as tumor initiating cells (tics), are a subset of tumor cells that persist within. Lastly, we provide perspectives and limitations in the field of deep learning based stem cell research. 2 deep learning for stem cell research 2.1 cnn based deep learning models for stem cell. Zhu et al. developed a deep learning based platform to predict neuron stem cells (nscs) differentiation using brightfield images without labelling (zhu et al., 2021). kusumoto et al. developed an automated deep learning based system to identify endothelial cells derived from induced pluripotent stem cells (kusumoto et al., 2018).
Pdf Deep Learning Models For Cancer Stem Cell Detection A Lastly, we provide perspectives and limitations in the field of deep learning based stem cell research. 2 deep learning for stem cell research 2.1 cnn based deep learning models for stem cell. Zhu et al. developed a deep learning based platform to predict neuron stem cells (nscs) differentiation using brightfield images without labelling (zhu et al., 2021). kusumoto et al. developed an automated deep learning based system to identify endothelial cells derived from induced pluripotent stem cells (kusumoto et al., 2018). A five way cell classification model (cnn1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. a four way cancer cell classification model (cnn2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. here, the cnn models were constructed by resnet inception v2. The detection of cancer stem like cells (cscs) is mainly based on molecular markers or functional tests giving a posteriori results. therefore label free and real time detection of single cscs.
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