A deep learning self-attention cross residual network with Info-WGANGP for mitotic cell identification in HEp-2 medical microscopic images
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[1] Ali Asghar Heidari,et al. Generative Adversarial Networks in Medical Image augmentation: A review , 2022, Comput. Biol. Medicine.
[2] Bingbing Ni,et al. MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification , 2021, Scientific Data.
[3] Aldo Marzullo,et al. Data augmentation using generative adversarial neural networks on brain structural connectivity in multiple sclerosis , 2021, Comput. Methods Programs Biomed..
[4] Tom Drummond,et al. Leveraging Regular Fundus Images for Training UWF Fundus Diagnosis Models via Adversarial Learning and Pseudo-Labeling , 2020, IEEE Transactions on Medical Imaging.
[5] João Paulo Papa,et al. Assisting Barrett's esophagus identification using endoscopic data augmentation based on Generative Adversarial Networks , 2020, Comput. Biol. Medicine.
[6] Sameer Antani,et al. Selective synthetic augmentation with HistoGAN for improved histopathology image classification , 2020, Medical Image Anal..
[7] Arnav Bhavsar,et al. Identification of HEp-2 specimen images with mitotic cell patterns , 2020 .
[8] Zhao Liu,et al. A GAN-based image synthesis method for skin lesion classification , 2020, Comput. Methods Programs Biomed..
[9] Hiroshi Fujita,et al. Investigation of pulmonary nodule classification using multi-scale residual network enhanced with 3DGAN-synthesized volumes , 2020, Radiological Physics and Technology.
[10] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[11] Walid Al-Dhabyani,et al. Dataset of breast ultrasound images , 2019, Data in brief.
[12] Changming Sun,et al. Deep Learning based HEp-2 Image Classification: A Comprehensive Review , 2019, Medical Image Anal..
[13] Arnav Bhavsar,et al. Detecting mitotic cells in HEp-2 images as anomalies via one class classifier , 2019, Comput. Biol. Medicine.
[14] Taghi M. Khoshgoftaar,et al. A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.
[15] Yuexiang Li,et al. HEp-Net: a smaller and better deep-learning network for HEp-2 cell classification , 2019, Comput. methods Biomech. Biomed. Eng. Imaging Vis..
[16] Manfred Herold,et al. Clinical relevance of HEp-2 indirect immunofluorescent patterns: the International Consensus on ANA patterns (ICAP) perspective , 2019, Annals of the rheumatic diseases.
[17] LinLin Shen,et al. Deep cross residual network for HEp-2 cell staining pattern classification , 2018, Pattern Recognit..
[18] Nassir Navab,et al. GANs for Medical Image Analysis , 2018, Artif. Intell. Medicine.
[19] Feng Zhou,et al. A deeply supervised residual network for HEp-2 cell classification via cross-modal transfer learning , 2018, Pattern Recognit..
[20] Daniel S. Kermany,et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning , 2018, Cell.
[21] G. Iannello,et al. The inter-observer reading variability in anti-nuclear antibodies indirect (ANA) immunofluorescence test: A multicenter evaluation and a review of the literature. , 2017, Autoimmunity reviews.
[22] Bo Hu,et al. Unsupervised Learning for Cell-Level Visual Representation in Histopathology Images With Generative Adversarial Networks , 2017, IEEE Journal of Biomedical and Health Informatics.
[23] LinLin Shen,et al. A Deep Residual Inception Network for HEp-2 Cell Classification , 2017, DLMIA/ML-CDS@MICCAI.
[24] Sabri Boughorbel,et al. Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric , 2017, PloS one.
[25] Shijian Lu,et al. Accurate HEp-2 cell classification based on Sparse Coding of Superpixels , 2016, Pattern Recognit. Lett..
[26] Gennady V. Ponomarev,et al. Classification of ANA HEp-2 slide images using morphological features of stained patterns , 2016, Pattern Recognit. Lett..
[27] Alessia Saggese,et al. Computer Aided Diagnosis for Anti-Nuclear Antibodies HEp-2 images: Progress and challenges , 2016, Pattern Recognit. Lett..
[28] Stephen J. McKenna,et al. An automated pattern recognition system for classifying indirect immunofluorescence images of HEp-2 cells and specimens , 2016, Pattern Recognit..
[29] B. Lovell,et al. Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset , 2015, Artif. Intell. Medicine.
[30] Lei Wang,et al. HEp-2 Cell Image Classification With Deep Convolutional Neural Networks , 2015, IEEE Journal of Biomedical and Health Informatics.
[31] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[32] Mario Vento,et al. Mitotic cells recognition in HEp-2 images , 2014, Pattern Recognit. Lett..
[33] Alessia Saggese,et al. Pattern recognition in stained HEp-2 cells: Where are we now? , 2014, Pattern Recognit..
[34] M. Fritzler,et al. Current Concepts and Future Directions for the Assessment of Autoantibodies to Cellular Antigens Referred to as Anti-Nuclear Antibodies , 2014, Journal of immunology research.
[35] M. Vento,et al. Benchmarking HEp-2 Cells Classification Methods , 2013, IEEE Transactions on Medical Imaging.
[36] Mario Vento,et al. Early experiences in mitotic cells recognition on HEp-2 slides , 2010, 2010 IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS).
[37] Francisco Herrera,et al. A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability , 2009, Soft Comput..
[38] Chong-Wah Ngo,et al. Evaluating bag-of-visual-words representations in scene classification , 2007, MIR '07.
[39] J. Demšar. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[40] Jitendra Malik,et al. Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.
[41] Yann LeCun,et al. Signature Verification Using A "Siamese" Time Delay Neural Network , 1993, Int. J. Pattern Recognit. Artif. Intell..
[42] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[43] M. Friedman. A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings , 1940 .
[44] Akio Gofuku,et al. Studying the Applicability of Generative Adversarial Networks on HEp-2 Cell Image Augmentation , 2021, IEEE Access.
[45] Arnav Bhavsar,et al. CNN based Mitotic HEp-2 Cell Image Detection , 2018, BIOIMAGING.
[46] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[47] Matti Pietikäinen,et al. A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..