S3-VAE: A novel Supervised-Source-Separation Variational AutoEncoder algorithm to discriminate tumor cell lines in time-lapse microscopy images

[1]  M. C. Comes,et al.  Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis , 2023, Communications Biology.

[2]  Himanshu K. Gajera,et al.  A comprehensive analysis of dermoscopy images for melanoma detection via deep CNN features , 2023, Biomed. Signal Process. Control..

[3]  Heye Zhang,et al.  Progressive Perception Learning for Main Coronary Segmentation in X-Ray Angiography , 2022, IEEE Transactions on Medical Imaging.

[4]  Zhijun Fang,et al.  An effective CNN and Transformer complementary network for medical image segmentation , 2022, Pattern Recognit..

[5]  L. Tahtamouni,et al.  Morphological signatures of actin organization in single cells accurately classify genetic perturbations using CNNs with transfer learning. , 2022, Soft matter.

[6]  K. Min,et al.  Attentional feature pyramid network for small object detection , 2022, Neural Networks.

[7]  J. Rojo-álvarez,et al.  Multivariate feature selection and autoencoder embeddings of ovarian cancer clinical and genetic data , 2022, Expert Syst. Appl..

[8]  Qiqiang Li,et al.  A novel concavity based method for automatic segmentation of touching cells in microfluidic chips , 2022, Expert Syst. Appl..

[9]  Mark A. Dane,et al.  A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis , 2022, Communications Biology.

[10]  Timothy R. Jackson,et al.  LIVECell—A large-scale dataset for label-free live cell segmentation , 2021, Nature Methods.

[11]  Arianna Mencattini,et al.  NeuriTES. Monitoring neurite changes through transfer entropy and semantic segmentation in bright-field time-lapse microscopy , 2021, Patterns.

[12]  Juan C. Caicedo,et al.  Image-based cell phenotyping with deep learning. , 2021, Current opinion in chemical biology.

[13]  T. Ikegami,et al.  Organization of a Latent Space structure in VAE/GAN trained by navigation data , 2021, Neural Networks.

[14]  Samuel Berryman,et al.  Image-based phenotyping of disaggregated cells using deep learning , 2020, Communications biology.

[15]  Arianna Mencattini,et al.  Deciphering Cancer Cell Behavior From Motility and Shape Features: Peer Prediction and Dynamic Selection to Support Cancer Diagnosis and Therapy , 2020, Frontiers in Oncology.

[16]  Shiping Wang,et al.  Deep clustering by maximizing mutual information in variational auto-encoder , 2020, Knowl. Based Syst..

[17]  Shaista Hussain,et al.  High-content image generation for drug discovery using generative adversarial networks , 2020, Neural Networks.

[18]  Moncef Gabbouj,et al.  Self-Organized Operational Neural Networks for Severe Image Restoration Problems , 2020, Neural Networks.

[19]  Vignesh Ram Somnath,et al.  Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations , 2019, ICLR 2019.

[20]  C Di Natale,et al.  The influence of spatial and temporal resolutions on the analysis of cell-cell interaction: a systematic study for time-lapse microscopy applications , 2019, Scientific Reports.

[21]  Rogelio Andrade Mancisidor,et al.  Learning Latent Representations of Bank Customers With The Variational Autoencoder , 2019, Expert Syst. Appl..

[22]  Arianna Mencattini,et al.  Learning Cancer-Related Drug Efficacy Exploiting Consensus in Coordinated Motility Within Cell Clusters , 2019, IEEE Transactions on Biomedical Engineering.

[23]  Fei Wang,et al.  Deep learning for healthcare: review, opportunities and challenges , 2018, Briefings Bioinform..

[24]  Ashok Prasad,et al.  TISMorph: A tool to quantify texture, irregularity and spreading of single cells , 2018, bioRxiv.

[25]  Jian Zhang,et al.  Convolutional Sparse Autoencoders for Image Classification , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Hossein Azizpour,et al.  Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays. , 2018, Cell systems.

[27]  Casper Kaae Sønderby,et al.  scVAE: Variational auto-encoders for single-cell gene expression data , 2018, bioRxiv.

[28]  Hong Zhao,et al.  A deep convolutional neural network for classification of red blood cells in sickle cell anemia , 2017, PLoS Comput. Biol..

[29]  Lassi Paavolainen,et al.  Data-analysis strategies for image-based cell profiling , 2017, Nature Methods.

[30]  E. Martinelli,et al.  3D Microfluidic model for evaluating immunotherapy efficacy by tracking dendritic cell behaviour toward tumor cells , 2017, Scientific Reports.

[31]  Erik Meijering,et al.  Imagining the future of bioimage analysis , 2016, Nature Biotechnology.

[32]  Christopher Burgess,et al.  beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.

[33]  Rangaraj M. Rangayyan,et al.  Contour-independent detection and classification of mammographic lesions , 2016, Biomed. Signal Process. Control..

[34]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Jieping Ye,et al.  Deep Model Based Transfer and Multi-Task Learning for Biological Image Analysis , 2015, IEEE Transactions on Big Data.

[36]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[37]  Corbin E. Meacham,et al.  Tumour heterogeneity and cancer cell plasticity , 2013, Nature.

[38]  J. Foekens,et al.  miRNA expression profiling of 51 human breast cancer cell lines reveals subtype and driver mutation-specific miRNAs , 2013, Breast Cancer Research.

[39]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[41]  David M. Simcha,et al.  Tackling the widespread and critical impact of batch effects in high-throughput data , 2010, Nature Reviews Genetics.

[42]  Loris Nanni,et al.  Local binary patterns variants as texture descriptors for medical image analysis , 2010, Artif. Intell. Medicine.

[43]  S. Bodovitz,et al.  Single cell analysis: the new frontier in 'omics'. , 2010, Trends in biotechnology.

[44]  Lani F. Wu,et al.  Cellular Heterogeneity: Do Differences Make a Difference? , 2010, Cell.

[45]  J. Massagué,et al.  Cancer Metastasis: Building a Framework , 2006, Cell.

[46]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[47]  Bin Yu,et al.  Boosting with early stopping: Convergence and consistency , 2005, math/0508276.

[48]  Darren J. Kerbyson,et al.  Size invariant circle detection , 1999, Image Vis. Comput..

[49]  Bernhard Schölkopf,et al.  Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..

[50]  J. Munkres ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .

[51]  D. Koundal,et al.  Fusion of U-Net and CNN model for segmentation and classification of skin lesion from dermoscopy images , 2023, Expert Syst. Appl..

[52]  R. Vinod Kumar,et al.  Segmentation of liver computed tomography images using dictionary-based snakes , 2022, International Journal of Biomedical Engineering and Technology.

[53]  Nicholas J. Higham,et al.  Cholesky factorization , 2009 .