Artificial Intelligence based Liver Portal Tract Region Identification and Quantification with Transplant Biopsy Whole-Slide Images
暂无分享,去创建一个
Jun Kong | George Teodoro | Fusheng Wang | A. Farris | Hanyi Yu | Nima Sharifai | Kun Jiang | Alton B. Farris
[1] Y. Ramot,et al. Microscope-Based Automated Quantification of Liver Fibrosis in Mice Using a Deep Learning Algorithm , 2021, Toxicologic pathology.
[2] Jerry L Prince,et al. A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises , 2020, Proceedings of the IEEE.
[3] Siamak Mehrkanoon,et al. SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture , 2020, Pattern Recognit. Lett..
[4] Jun Kong,et al. Quantitative assessment of liver fibrosis by digital image analysis reveals correlation with qualitative clinical fibrosis staging in liver transplant patients , 2020, PloS one.
[5] Harvey Lui,et al. Dense-UNet: a novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network. , 2020, Quantitative imaging in medicine and surgery.
[6] Ting Zhang,et al. SD-UNet: Stripping down U-Net for Segmentation of Biomedical Images on Platforms with Low Computational Budgets , 2020, Diagnostics.
[7] Mohammad Sohel Rahman,et al. MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation , 2019, Neural Networks.
[8] Ross B. Girshick,et al. Mask R-CNN , 2017, 1703.06870.
[9] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[10] Won-Chul Bang,et al. Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network , 2019, European Radiology.
[11] Geoffrey G. Zhang,et al. Detection and classification the breast tumors using mask R-CNN on sonograms , 2019, Medicine.
[12] Jun Kong,et al. Liver Steatosis Segmentation With Deep Learning Methods , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).
[13] Yassine Ruichek,et al. Survey on semantic segmentation using deep learning techniques , 2019, Neurocomputing.
[14] Chris Yakopcic,et al. A State-of-the-Art Survey on Deep Learning Theory and Architectures , 2019, Electronics.
[15] Albert Montillo,et al. Deep learning convolutional neural networks for the estimation of liver fibrosis severity from ultrasound texture , 2019, Medical Imaging.
[16] Yao Lu,et al. RIC-Unet: An Improved Neural Network Based on Unet for Nuclei Segmentation in Histology Images , 2019, IEEE Access.
[17] Hanry Yu,et al. Deep learning enables automated scoring of liver fibrosis stages , 2018, Scientific Reports.
[18] Jin-Young Choi,et al. Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent-enhanced CT Images in the Liver. , 2018, Radiology.
[19] Hui Yu,et al. Segmentation of Lung Nodule in CT Images Based on Mask R-CNN , 2018, 2018 9th International Conference on Awareness Science and Technology (iCAST).
[20] In-So Kweon,et al. CBAM: Convolutional Block Attention Module , 2018, ECCV.
[21] Peter H. N. de With,et al. Stain normalization of histopathology images using generative adversarial networks , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[22] O. Abe,et al. Deep learning for staging liver fibrosis on CT: a pilot study , 2018, European Radiology.
[23] A. Wee,et al. Progression and regression of fibrosis in viral hepatitis in the treatment era: the Beijing classification , 2018, Modern Pathology.
[24] Guang Yang,et al. A two-stage 3D Unet framework for multi-class segmentation on full resolution image , 2018, ArXiv.
[25] O. Abe,et al. Liver Fibrosis: Deep Convolutional Neural Network for Staging by Using Gadoxetic Acid-enhanced Hepatobiliary Phase MR Images. , 2017, Radiology.
[26] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] Yu Hu,et al. Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B , 2017, Comput. Biol. Medicine.
[28] Zenghui Wang,et al. Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review , 2017, Neural Computation.
[29] George Papandreou,et al. Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.
[30] David Dagan Feng,et al. Stacked fully convolutional networks with multi-channel learning: application to medical image segmentation , 2017, The Visual Computer.
[31] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[32] François Chollet,et al. Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Christopher Joseph Pal,et al. The Importance of Skip Connections in Biomedical Image Segmentation , 2016, LABELS/DLMIA@MICCAI.
[34] Nassir Navab,et al. Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images , 2016, IEEE Transactions on Medical Imaging.
[35] Francesco Visin,et al. A guide to convolution arithmetic for deep learning , 2016, ArXiv.
[36] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[38] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[39] Nassir Navab,et al. Structure-preserved color normalization for histological images , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).
[40] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[41] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[42] Yukako Yagi,et al. Staining Correction in Digital Pathology by Utilizing a Dye Amount Table , 2015, Journal of Digital Imaging.
[43] Roy E. Welsch,et al. Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification , 2014, Scientific Reports.
[44] Nico Karssemeijer,et al. Quantitative analysis of stain variability in histology slides and an algorithm for standardization , 2014, Medical Imaging.
[45] Hanry Yu,et al. qFibrosis: a fully-quantitative innovative method incorporating histological features to facilitate accurate fibrosis scoring in animal model and chronic hepatitis B patients. , 2014, Journal of hepatology.
[46] Yukako Yagi,et al. Color standardization in whole slide imaging using a color calibration slide , 2014, Journal of pathology informatics.
[47] May D. Wang,et al. Comparison of normalization algorithms for cross-batch color segmentation of histopathological images , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[48] Luca Maria Gambardella,et al. Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.
[49] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[50] R. Standish,et al. An appraisal of the histopathological assessment of liver fibrosis , 2006, Gut.