{"list":"other","total":62,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1911.05722","title":"Momentum Contrast for Unsupervised Visual Representation Learning","created":"2019-11-13","cited_by_count":7513},{"id":"1411.4038","title":"Fully Convolutional Networks for Semantic Segmentation","created":"2014-11-14","cited_by_count":4859},{"id":"1611.07004","title":"Image-to-Image Translation with Conditional Adversarial Networks","created":"2016-11-21","cited_by_count":4527},{"id":"1409.4842","title":"Going Deeper with Convolutions","created":"2014-09-17","cited_by_count":4241},{"id":"2102.09672","title":"Improved Denoising Diffusion Probabilistic Models","created":"2021-02-18","cited_by_count":3761},{"id":"1606.00915","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","created":"2016-06-02","cited_by_count":3241},{"id":"1505.07818","title":"Domain-Adversarial Training of Neural Networks","created":"2015-05-28","cited_by_count":3044},{"id":"1606.04797","title":"V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation","created":"2016-06-15","cited_by_count":2292},{"id":"1706.05587","title":"Rethinking Atrous Convolution for Semantic Image Segmentation","created":"2017-06-17","cited_by_count":2161},{"id":"2102.04306","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","created":"2021-02-08","cited_by_count":1558},{"id":"1807.03039","title":"Glow: Generative Flow with Invertible 1x1 Convolutions","created":"2018-07-09","cited_by_count":1545},{"id":"1703.06907","title":"Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World","created":"2017-03-20","cited_by_count":1498},{"id":"1711.03213","title":"CyCADA: Cycle-Consistent Adversarial Domain Adaptation","created":"2017-11-08","cited_by_count":1082},{"id":"1906.02629","title":"When Does Label Smoothing Help?","created":"2019-06-06","cited_by_count":988},{"id":"1608.02192","title":"Playing for Data: Ground Truth from Computer Games","created":"2016-08-07","cited_by_count":818},{"id":"1705.07057","title":"Masked Autoregressive Flow for Density Estimation","created":"2017-05-19","cited_by_count":684},{"id":"1802.03601","title":"Deep Visual Domain Adaptation: A Survey","created":"2018-02-10","cited_by_count":635},{"id":"2106.05735","title":"The Medical Segmentation Decathlon","created":"2021-06-10","cited_by_count":628},{"id":"1707.03237","title":"Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations","created":"2017-07-11","cited_by_count":620},{"id":"2004.13649","title":"Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels","created":"2020-04-28","cited_by_count":585},{"id":"1412.7062","title":"Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs","created":"2014-12-22","cited_by_count":554},{"id":"1505.04366","title":"Learning Deconvolution Network for Semantic Segmentation","created":"2015-05-17","cited_by_count":365},{"id":"1502.03492","title":"Gradient-based Hyperparameter Optimization through Reversible Learning","created":"2015-02-11","cited_by_count":360},{"id":"2006.10511","title":"Contrastive learning of global and local features for medical image segmentation with limited annotations","created":"2020-06-18","cited_by_count":309},{"id":"1804.06516","title":"Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization","created":"2018-04-18","cited_by_count":294},{"id":"1612.07828","title":"Learning from Simulated and Unsupervised Images through Adversarial Training","created":"2016-12-22","cited_by_count":288},{"id":"2104.06490","title":"DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort","created":"2021-04-13","cited_by_count":264},{"id":"1905.00397","title":"Fast AutoAugment","created":"2019-05-01","cited_by_count":243},{"id":"2107.09559","title":"SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining","created":"2021-07-20","cited_by_count":237},{"id":"2104.05833","title":"Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization","created":"2021-04-12","cited_by_count":141},{"id":"2004.10282","title":"SynthMorph: learning contrast-invariant registration without acquired images","created":"2020-04-21","cited_by_count":140},{"id":"1902.09383","title":"Data augmentation using learned transformations for one-shot medical image segmentation","created":"2019-02-25","cited_by_count":138},{"id":"1801.02385","title":"Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification","created":"2018-01-08","cited_by_count":136},{"id":"2203.09974","title":"SynthStrip: Skull-Stripping for Any Brain Image","created":"2022-03-18","cited_by_count":127},{"id":"1612.08894","title":"Unsupervised domain adaptation in brain lesion segmentation with adversarial networks","created":"2016-12-28","cited_by_count":125},{"id":"1901.08211","title":"Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation","created":"2019-01-24","cited_by_count":121},{"id":"1703.04782","title":"Online Learning Rate Adaptation with Hypergradient Descent","created":"2017-03-14","cited_by_count":118},{"id":"1909.11512","title":"Synthetic Data for Deep Learning","created":"2019-09-25","cited_by_count":113},{"id":"2004.04668","title":"Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation","created":"2020-04-09","cited_by_count":102},{"id":"1802.09655","title":"Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network","created":"2018-02-27","cited_by_count":97},{"id":"1911.06987","title":"Faster AutoAugment: Learning Augmentation Strategies using Backpropagation","created":"2019-11-16","cited_by_count":96},{"id":"2103.04379","title":"Repurposing GANs for One-shot Semantic Part Segmentation","created":"2021-03-07","cited_by_count":88},{"id":"2108.06227","title":"SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation","created":"2021-08-13","cited_by_count":86},{"id":"1804.01118","title":"Synthesizing Programs for Images using Reinforced Adversarial Learning","created":"2018-04-03","cited_by_count":72},{"id":"1810.06498","title":"SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth","created":"2018-10-15","cited_by_count":68},{"id":"1810.00236","title":"Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images","created":"2018-09-29","cited_by_count":56},{"id":"2209.02032","title":"Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets","created":"2022-09-05","cited_by_count":51},{"id":"1706.07581","title":"Cross-validation failure: small sample sizes lead to large error bars","created":"2017-06-23","cited_by_count":43},{"id":"1902.05396","title":"Semi-Supervised and Task-Driven Data Augmentation","created":"2019-02-11","cited_by_count":40},{"id":"2206.01369","title":"Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation","created":"2022-06-03","cited_by_count":22},{"id":"2108.03429","title":"Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation","created":"2021-08-07","cited_by_count":20},{"id":"2202.01337","title":"Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls","created":"2022-02-01","cited_by_count":12},{"id":"2204.05546","title":"Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation","created":"2022-04-12","cited_by_count":11},{"id":"2106.04767","title":"Ex uno plures: Splitting One Model into an Ensemble of Subnetworks","created":"2021-06-09","cited_by_count":5},{"id":"2209.07547","title":"One-Shot Synthesis of Images and Segmentation Masks","created":"2022-09-15","cited_by_count":4},{"id":"1807.11598","title":"Pulse Sequence Resilient Fast Brain Segmentation","created":"2018-07-30","cited_by_count":2},{"id":"2306.02901","title":"A Vessel-Segmentation-Based CycleGAN for Unpaired Multi-modal Retinal Image Synthesis","created":"2023-06-05","cited_by_count":1}],"next_cursor":null}