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ViTAMIn‐O: Democratizing Computer Vision‐Based Machine Learning for Stem Cell Research
Advanced Intelligent Systems, EarlyView.This study introduces a fully open generalist computer vision model for predicting the differentiation outcomes of stem cell‐based model systems, together with its code‐free deployment platform, ColabViTAMIn‐O. It is benchmarked against various architectures, datasets, and training protocols.
Ferhat Hamurcu +11 more
wiley +1 more source
Advanced Intelligent Systems, EarlyView.This review highlights recent advances in automated iPSC culture systems integrating robotics, advanced imaging, artificial intelligence, and process control. These technologies improve reproducibility, scalability, and quality in cell expansion, differentiation, and real‐time monitoring, while emerging AI‐enabled and closed‐loop approaches offer new ...
Hyoryong Lee +3 more
wiley +1 more source
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C-CNN: Contourlet Convolutional Neural Networks
IEEE Transactions on Neural Networks and Learning Systems, 2021Extracting effective features is always a challenging problem for texture classification because of the uncertainty of scales and the clutter of textural patterns. For texture classification, spectral analysis is traditionally employed in the frequency domain.
Mengkun Liu, Shuyuan Yang, Fang Liu
exaly +4 more sources
Convolutional Neural Networks (CNN)
2021Convolutional neural networks (CNN or ConvNet) are a specific type of neural networks for processing grid-like data such as images and time series. In healthcare applications, the CNN models are widely used in automatic feature learning and disease classification from medical images, for example, automatic classification of skin lesions, detection of ...
Cao Xiao, Jimeng Sun
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CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization [PDF]
IEEE Transactions on Visualization and Computer Graphics, 2021 11 pages, 14 figures, to be presented at IEEE VIS 2020. For a demo video, see https://youtu.be/HnWIHWFbuUQ . For a live demo, visit https://poloclub.github.io/cnn-explainer/
Minsuk Kahng, Haekyu Park, Zijie J Wang
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Cellular neural network friendly convolutional neural networks — CNNs with CNNs
Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017, 2017This paper discusses the development and evaluation of a Cellular Neural Network (CeNN) friendly deep learning network for solving the MNIST digit recognition problem. Prior work has shown that CeNNs leveraging emerging technologies such as tunnel transistors can improve energy or EDP of CeNNs, while simultaneously offering richer/more complex ...
András Horváth +4 more
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Convolutional Neural Networks (CNN)
2022Deep learning is one of the main technologies of machine learning. With deep learning, this chapter is talking about algorithms capable of mimicking the actions of the human brain through artificial neural networks. Compared to other algorithmic structures, neural networks have great advantages: first, their structure based on the stacking of non ...
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A Reversible-Logic based Architecture for Convolutional Neural Network (CNN)
2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), 2021Convolutional-Neural-Network (CNN) is a deep learning model, which is used extensively to solve complex image classification or computer vision problems. CNN and more complex architecture variants of it such as vggX, GoogleNet, ImageNet, etc. are widely used in various application domains such as object detection, self-driving cars, instance ...
Kasem Khalil +3 more
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Convolutional Neural Network (CNN): The architecture and applications
Applied Journal of Physical Science, 2022The human brain is made up of several hundreds of billions of interconnected neurons that process information in parallel. Researchers in the field of artificial intelligence have successfully demonstrated a considerable level of intelligence on chips and this has been termed Neural Networks (NNs).
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Convolutional Neural Networks (CNN)
2019Convolutional neural networks (CNN) are a specific type of neural network systems that are particularly suited for computer vision problems such as image recognition. In such tasks, the dataset is represented as a 2-D grid of pixels. See Figure 35-1.
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