{"list":"other","total":81,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"1502.03167","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","created":"2015-02-11","cited_by_count":7005},{"id":"1608.06993","title":"Densely Connected Convolutional Networks","created":"2016-08-25","cited_by_count":6720},{"id":"1502.01852","title":"Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification","created":"2015-02-06","cited_by_count":3611},{"id":"2201.03545","title":"A ConvNet for the 2020s","created":"2022-01-10","cited_by_count":3579},{"id":"1606.08415","title":"Gaussian Error Linear Units (GELUs)","created":"2016-06-27","cited_by_count":3502},{"id":"1603.08155","title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution","created":"2016-03-27","cited_by_count":2694},{"id":"1606.04797","title":"V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation","created":"2016-06-15","cited_by_count":2292},{"id":"1704.00028","title":"Improved Training of Wasserstein GANs","created":"2017-03-31","cited_by_count":2235},{"id":"1609.04802","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","created":"2016-09-15","cited_by_count":1908},{"id":"2108.10257","title":"SwinIR: Image Restoration Using Swin Transformer","created":"2021-08-23","cited_by_count":1887},{"id":"2111.09881","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","created":"2021-11-18","cited_by_count":1627},{"id":"1707.02921","title":"Enhanced Deep Residual Networks for Single Image Super-Resolution","created":"2017-07-10","cited_by_count":1552},{"id":"1807.02758","title":"Image Super-Resolution Using Very Deep Residual Channel Attention Networks","created":"2018-07-08","cited_by_count":1264},{"id":"1809.00219","title":"ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks","created":"2018-09-01","cited_by_count":1254},{"id":"1501.00092","title":"Image Super-Resolution Using Deep Convolutional Networks","created":"2014-12-31","cited_by_count":1224},{"id":"2105.05537","title":"Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation","created":"2021-05-12","cited_by_count":1222},{"id":"1702.05747","title":"A Survey on Deep Learning in Medical Image Analysis","created":"2017-02-19","cited_by_count":1186},{"id":"2106.15282","title":"Cascaded Diffusion Models for High Fidelity Image Generation","created":"2021-05-30","cited_by_count":1163},{"id":"1511.04587","title":"Accurate Image Super-Resolution Using Very Deep Convolutional Networks","created":"2015-11-14","cited_by_count":903},{"id":"2012.00364","title":"Pre-Trained Image Processing Transformer","created":"2020-12-01","cited_by_count":893},{"id":"2106.03106","title":"Uformer: A General U-Shaped Transformer for Image Restoration","created":"2021-06-06","cited_by_count":866},{"id":"2106.02034","title":"DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification","created":"2021-06-03","cited_by_count":864},{"id":"1802.08797","title":"Residual Dense Network for Image Super-Resolution","created":"2018-02-24","cited_by_count":799},{"id":"1701.04128","title":"Understanding the Effective Receptive Field in Deep Convolutional Neural Networks","created":"2017-01-15","cited_by_count":553},{"id":"1811.02629","title":"Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge","created":"2018-11-05","cited_by_count":511},{"id":"2111.14791","title":"Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis","created":"2021-11-29","cited_by_count":422},{"id":"2104.14951","title":"SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models","created":"2021-04-30","cited_by_count":415},{"id":"1708.08987","title":"Deep Learning for Medical Image Analysis","created":"2017-08-17","cited_by_count":401},{"id":"1608.00367","title":"Accelerating the Super-Resolution Convolutional Neural Network","created":"2016-08-01","cited_by_count":394},{"id":"1902.06068","title":"Deep Learning for Image Super-resolution: A Survey","created":"2019-02-16","cited_by_count":369},{"id":"1511.04491","title":"Deeply-Recursive Convolutional Network for Image Super-Resolution","created":"2015-11-14","cited_by_count":317},{"id":"1712.06087","title":"\"Zero-Shot\" Super-Resolution using Deep Internal Learning","created":"2017-12-17","cited_by_count":286},{"id":"2008.08767","title":"Single Image Super-Resolution via a Holistic Attention Network","created":"2020-08-20","cited_by_count":265},{"id":"1809.07294","title":"Generative Adversarial Network in Medical Imaging: A Review","created":"2018-09-19","cited_by_count":257},{"id":"1708.02209","title":"MemNet: A Persistent Memory Network for Image Restoration","created":"2017-08-07","cited_by_count":244},{"id":"2111.07624","title":"Attention Mechanisms in Computer Vision: A Survey","created":"2021-11-15","cited_by_count":232},{"id":"1805.07071","title":"Multi-level Wavelet-CNN for Image Restoration","created":"2018-05-18","cited_by_count":184},{"id":"1903.00875","title":"Meta-SR: A Magnification-Arbitrary Network for Super-Resolution","created":"2019-03-03","cited_by_count":181},{"id":"1708.00961","title":"Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss","created":"2017-08-03","cited_by_count":162},{"id":"1808.03344","title":"Deep Learning for Single Image Super-Resolution: A Brief Review","created":"2018-08-09","cited_by_count":158},{"id":"2203.06717","title":"Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs","created":"2022-03-13","cited_by_count":155},{"id":"2108.11084","title":"Transformer for Single Image Super-Resolution","created":"2021-08-25","cited_by_count":150},{"id":"2106.02852","title":"Patch Slimming for Efficient Vision Transformers","created":"2021-06-05","cited_by_count":133},{"id":"1803.09454","title":"Fast and Accurate Single Image Super-Resolution via Information Distillation Network","created":"2018-03-26","cited_by_count":121},{"id":"1705.08302","title":"Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation","created":"2017-05-22","cited_by_count":109},{"id":"1708.01155","title":"Deep MR to CT Synthesis using Unpaired Data","created":"2017-08-03","cited_by_count":86},{"id":"1808.04256","title":"CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)","created":"2018-08-10","cited_by_count":82},{"id":"1809.06222","title":"GANs for Medical Image Analysis","created":"2018-09-13","cited_by_count":81},{"id":"1412.0623","title":"Material Recognition in the Wild with the Materials in Context Database","created":"2014-12-01","cited_by_count":79},{"id":"1803.01417","title":"Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network","created":"2018-03-04","cited_by_count":64},{"id":"1906.12021","title":"Densely Residual Laplacian Super-Resolution","created":"2019-06-28","cited_by_count":57},{"id":"2201.03230","title":"Swin Transformer for Fast MRI","created":"2022-01-10","cited_by_count":56},{"id":"2004.12537","title":"Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation","created":"2020-04-27","cited_by_count":53},{"id":"2201.09376","title":"ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer","created":"2022-01-23","cited_by_count":52},{"id":"1801.02728","title":"Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks","created":"2018-01-08","cited_by_count":46},{"id":"1908.01612","title":"Multi-Contrast Super-Resolution MRI Through a Progressive Network","created":"2019-08-05","cited_by_count":45},{"id":"2202.14009","title":"SUNet: Swin Transformer UNet for Image Denoising","created":"2022-02-28","cited_by_count":44},{"id":"2202.12165","title":"Transformers in Medical Image Analysis: A Review","created":"2022-02-24","cited_by_count":41},{"id":"2011.04566","title":"MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution","created":"2020-11-09","cited_by_count":38},{"id":"2203.13963","title":"Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution","created":"2022-03-26","cited_by_count":30},{"id":"1810.06453","title":"Channel Splitting Network for Single MR Image Super-Resolution","created":"2018-10-15","cited_by_count":26},{"id":"2011.04145","title":"Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain","created":"2020-11-09","cited_by_count":24},{"id":"1805.02704","title":"Image Super-Resolution via Dual-State Recurrent Networks","created":"2018-05-07","cited_by_count":22},{"id":"2204.13286","title":"Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer","created":"2022-04-28","cited_by_count":22},{"id":"2204.08913","title":"Self-Calibrated Efficient Transformer for Lightweight Super-Resolution","created":"2022-04-19","cited_by_count":20},{"id":"1812.11440","title":"Brain MRI super-resolution using 3D generative adversarial networks","created":"2018-12-29","cited_by_count":15},{"id":"1703.00035","title":"Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging","created":"2017-02-28","cited_by_count":10},{"id":"2010.08357","title":"VolumeNet: A Lightweight Parallel Network for Super-Resolution of Medical Volumetric Data","created":"2020-10-16","cited_by_count":9},{"id":"2211.10043","title":"Vision Transformers in Medical Imaging: A Review","created":"2022-11-18","cited_by_count":8},{"id":"1901.03419","title":"How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach","created":"2019-01-10","cited_by_count":7},{"id":"2002.07874","title":"Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks","created":"2020-02-14","cited_by_count":7},{"id":"2108.03920","title":"FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution","created":"2021-08-09","cited_by_count":7},{"id":"1810.06693","title":"Lesion Focused Super-Resolution","created":"2018-10-15","cited_by_count":5},{"id":"1611.07718","title":"Deep Convolutional Neural Networks with Merge-and-Run Mappings","created":"2016-11-23","cited_by_count":4},{"id":"2212.02028","title":"Double U-Net for Super-Resolution and Segmentation of Live Cell Images","created":"2022-12-05","cited_by_count":1}],"next_cursor":null}