Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning
暂无分享,去创建一个
Michael Schmitt | Juergen Popp | Tobias Meyer | Thomas Bocklitz | Michael Vieth | Andreas Stallmach | Pranita Pradhan | Maximilian Waldner | M. Vieth | M. Schmitt | T. Bocklitz | M. Waldner | J. Popp | A. Stallmach | T. Meyer | Pranita Pradhan
[1] Jürgen Popp,et al. Multimodal Imaging Spectroscopy of Tissue. , 2015, Annual review of analytical chemistry.
[2] Janne Heikkilä,et al. Towards Virtual H&E Staining of Hyperspectral Lung Histology Images Using Conditional Generative Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).
[3] B Dietzek,et al. Multimodal mapping of human skin , 2013, The British journal of dermatology.
[4] A. Ozcan,et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning , 2018, Nature Biomedical Engineering.
[5] Riccardo Cicchi,et al. Multimodal nonlinear microscopy: A powerful label-free method for supporting standard diagnostics on biological tissues , 2014 .
[6] Nicholas Ayache,et al. The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration , 1998, MICCAI.
[7] Lin Shi,et al. Color quantification for evaluation of stained tissues , 2011, Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[8] Francesco S. Pavone,et al. Multimodal image analysis in tissue diagnostics for skin melanoma , 2018 .
[9] Juho Kannala,et al. Deep learning for magnification independent breast cancer histopathology image classification , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).
[10] Tejas Sudharshan Mathai,et al. Accurate Tissue Interface Segmentation via Adversarial Pre-Segmentation of Anterior Segment OCT Images , 2019, Biomedical optics express.
[11] Li Fei-Fei,et al. Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.
[12] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[13] Michael Schmitt,et al. Semantic Segmentation of Non-linear Multimodal Images for Disease Grading of Inflammatory Bowel Disease: A SegNet-based Application , 2019, ICPRAM.
[14] Jürgen Popp,et al. Nonlinear microscopy, infrared, and Raman microspectroscopy for brain tumor analysis. , 2011, Journal of biomedical optics.
[15] Jürgen Popp,et al. Beyond endoscopic assessment in inflammatory bowel disease: real-time histology of disease activity by non-linear multimodal imaging , 2016, Scientific Reports.
[16] A. Ozcan,et al. Cross-Modality Deep Learning Achieves Super-Resolution in Fluorescence Microscopy , 2019, 2019 Conference on Lasers and Electro-Optics (CLEO).
[17] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[18] Yibo Zhang,et al. Deep learning‐based color holographic microscopy , 2019, Journal of biophotonics.
[19] Zhou Wang,et al. Translation insensitive image similarity in complex wavelet domain , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..
[20] Todd C. Hollon,et al. Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy , 2017, Nature Biomedical Engineering.
[21] Jürgen Popp,et al. Pseudo-HE images derived from CARS/TPEF/SHG multimodal imaging in combination with Raman-spectroscopy as a pathological screening tool , 2016, BMC Cancer.
[22] Jia Li,et al. Automatic Sketch Colorization with Tandem Conditional Adversarial Networks , 2018, 2018 11th International Symposium on Computational Intelligence and Design (ISCID).
[23] Xinjian Chen,et al. Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN. , 2018, Biomedical optics express.
[24] Helmut Neumann,et al. Label-free imaging of inflammatory bowel disease using multiphoton microscopy. , 2013, Gastroenterology.
[25] Bahram Parvin,et al. Automated Histology Analysis: Opportunities for signal processing , 2015, IEEE Signal Processing Magazine.
[26] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[27] Su Ruan,et al. Medical Image Synthesis with Context-Aware Generative Adversarial Networks , 2016, MICCAI.
[28] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Hiroshi Ishikawa,et al. Retinal optical coherence tomography image enhancement via deep learning. , 2018, Biomedical optics express.
[30] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[31] Ian J. Goodfellow,et al. NIPS 2016 Tutorial: Generative Adversarial Networks , 2016, ArXiv.
[32] Xun Xu,et al. Improved cGAN based linear lesion segmentation in high myopia ICGA images. , 2019, Biomedical optics express.
[33] B. Dietzek,et al. Detection and Discrimination of Non-Melanoma Skin Cancer by Multimodal Imaging , 2013, Healthcare.
[34] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[35] Tomer Michaeli,et al. Deep-STORM: super-resolution single-molecule microscopy by deep learning , 2018, 1801.09631.
[36] Jürgen Popp,et al. Multimodal nonlinear microscopy of head and neck carcinoma — toward surgery assisting frozen section analysis , 2016, Head & neck.
[37] Ghassan Hamarneh,et al. Adversarial Stain Transfer for Histopathology Image Analysis , 2018, IEEE Transactions on Medical Imaging.
[38] Peng Fei,et al. High-throughput, high-resolution deep learning microscopy based on registration-free generative adversarial network. , 2019, Biomedical optics express.
[39] Qianni Zhang,et al. GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis , 2019, ArXiv.
[40] Jürgen Popp,et al. Multimodal nonlinear microscopic investigations on head and neck squamous cell carcinoma: Toward intraoperative imaging , 2013, Head & neck.
[41] Bin Li,et al. Detection of exudates in fundus photographs with imbalanced learning using conditional generative adversarial network. , 2018, Biomedical optics express.
[42] Hyunseok Min,et al. Neural Stain-Style Transfer Learning using GAN for Histopathological Images , 2017, ArXiv.
[43] Jie Tian,et al. Improved generative adversarial networks using the total gradient loss for the resolution enhancement of fluorescence images. , 2019, Biomedical optics express.
[44] Shuxia Guo,et al. Deep learning a boon for biophotonics? , 2020, Journal of biophotonics.