A deep learning self-attention cross residual network with Info-WGANGP for mitotic cell identification in HEp-2 medical microscopic images

[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..