Automatic label‐free detection of breast cancer using nonlinear multimodal imaging and the convolutional neural network ResNet50
Nairveen Ali,Nairveen Ali,Elsie Quansah,Elsie Quansah,Katarina Köhler,Tobias Meyer,Tobias Meyer,Michael Schmitt,Michael Schmitt,Jürgen Popp,Axel Niendorf,Thomas Bocklitz,Thomas Bocklitz +12 more
- Vol. 1
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The article was published on 2019-12-01 and is currently open access. It has received 26 citations till now. The article focuses on the topics: Deep learning & Convolutional neural network.read more
Citations
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Journal ArticleDOI
Deep learning a boon for biophotonics
Pranita Pradhan,Pranita Pradhan,Shuxia Guo,Shuxia Guo,Oleg Ryabchykov,Oleg Ryabchykov,Juergen Popp,Juergen Popp,Thomas Bocklitz,Thomas Bocklitz +9 more
TL;DR: The possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement, and the potential use ofDeep learning for spectroscopic data including spectral data preprocessing and spectral classification are discussed.
Journal ArticleDOI
Deep Learning for Biospectroscopy and Biospectral Imaging: State-of-the-Art and Perspectives.
TL;DR: This Feature focuses on the emerging applications of deep learning in the data preprocessing, feature detection, and modeling of the biological samples for spectral analysis and spectroscopic imaging.
Journal ArticleDOI
Deep learning for 'artefact' removal in infrared spectroscopy.
Shuxia Guo,Shuxia Guo,Thomas G. Mayerhöfer,Thomas G. Mayerhöfer,Susanne Pahlow,Susanne Pahlow,Uwe Hübner,Jürgen Popp,Jürgen Popp,Thomas Bocklitz,Thomas Bocklitz +10 more
TL;DR: An artefact removal approach based on a deep convolutional neural network (CNN), specifically a 1-dimensional U-shape Convolutional Neural network (1D U-Net) is proposed and based on poly(methyl methacrylate) (PMMA) as materials, and it is demonstrated that the network was able to retrieve the absorbance very well, even in cases where the absorbsance is completely overwhelmed by extremely large 'artefacts'.
Journal ArticleDOI
Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors.
Nairveen Ali,Nairveen Ali,Christian Bolenz,Tilman Todenhöfer,Arnulf Stenzel,Peer Deetmar,Martin Kriegmair,Thomas Knoll,Stefan Porubsky,Arndt Hartmann,Jürgen Popp,Jürgen Popp,Maximilian C. Kriegmair,Thomas Bocklitz,Thomas Bocklitz +14 more
TL;DR: In this paper, a pre-trained convolutional neural network was used to predict image malignancy, invasiveness, and grading, and the results indicated that the classification sensitivity and specificity of malignant lesions are 95.77% and 87.84% respectively, while the mean sensitivity and mean specificity of tumor invasion are 88% and 96.56%, respectively.
References
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Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
Freddie Bray,Jacques Ferlay,Isabelle Soerjomataram,Rebecca L. Siegel,Lindsey A. Torre,Ahmedin Jemal +5 more
TL;DR: A status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions.
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Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
Proceedings ArticleDOI
Densely Connected Convolutional Networks
TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
Journal ArticleDOI
Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.
Ignacio Arganda-Carreras,Ignacio Arganda-Carreras,Verena Kaynig,Curtis Rueden,Kevin W. Eliceiri,Johannes Schindelin,Albert Cardona,H. Sebastian Seung +7 more
TL;DR: The Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically, is introduced.
Journal ArticleDOI
U-Net: deep learning for cell counting, detection, and morphometry
Thorsten Falk,Dominic Mai,Robert Bensch,Özgün Çiçek,Ahmed Abdulkadir,Ahmed Abdulkadir,Yassine Marrakchi,Anton Böhm,Jan Deubner,Zoe Jäckel,Katharina Seiwald,Alexander Dovzhenko,Olaf Tietz,Cristina Dal Bosco,Sean Walsh,Deniz Saltukoglu,Tuan Leng Tay,Marco Prinz,Klaus Palme,Matias Simons,Ilka Diester,Thomas Brox,Olaf Ronneberger +22 more
TL;DR: An ImageJ plugin is presented that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service.