{"list":"other","total":68,"rows":[{"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":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"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":"1608.06993","title":"Densely Connected Convolutional Networks","created":"2016-08-25","cited_by_count":6720},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"2204.14198","title":"Flamingo: a Visual Language Model for Few-Shot Learning","created":"2022-04-29","cited_by_count":4386},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"1606.04797","title":"V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation","created":"2016-06-15","cited_by_count":2292},{"id":"1606.04080","title":"Matching Networks for One Shot Learning","created":"2016-06-13","cited_by_count":2255},{"id":"2102.04306","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","created":"2021-02-08","cited_by_count":1558},{"id":"2111.02080","title":"An Explanation of In-context Learning as Implicit Bayesian Inference","created":"2021-11-03","cited_by_count":938},{"id":"1810.04650","title":"Multi-Task Learning as Multi-Objective Optimization","created":"2018-10-10","cited_by_count":821},{"id":"1809.05231","title":"VoxelMorph: A Learning Framework for Deformable Medical Image Registration","created":"2018-09-14","cited_by_count":662},{"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":"2107.02314","title":"The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification","created":"2021-07-05","cited_by_count":518},{"id":"1901.04056","title":"The Liver Tumor Segmentation Benchmark (LiTS)","created":"2019-01-13","cited_by_count":509},{"id":"1908.06391","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","created":"2019-08-18","cited_by_count":449},{"id":"1511.05547","title":"Return of Frustratingly Easy Domain Adaptation","created":"2015-11-17","cited_by_count":391},{"id":"1812.06499","title":"HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images","created":"2018-12-16","cited_by_count":388},{"id":"1811.08883","title":"Rethinking ImageNet Pre-training","created":"2018-11-21","cited_by_count":377},{"id":"1902.09063","title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","created":"2019-02-25","cited_by_count":373},{"id":"1902.07208","title":"Transfusion: Understanding Transfer Learning for Medical Imaging","created":"2019-02-14","cited_by_count":368},{"id":"2206.08023","title":"AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation","created":"2022-06-16","cited_by_count":351},{"id":"1602.04433","title":"Unsupervised Domain Adaptation with Residual Transfer Networks","created":"2016-02-14","cited_by_count":341},{"id":"2212.02499","title":"Images Speak in Images: A Generalist Painter for In-Context Visual Learning","created":"2022-12-05","cited_by_count":318},{"id":"2001.06535","title":"CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation","created":"2020-01-17","cited_by_count":309},{"id":"1603.05959","title":"Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation","created":"2016-03-18","cited_by_count":263},{"id":"1908.06948","title":"Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography","created":"2019-08-16","cited_by_count":244},{"id":"2010.14808","title":"AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?","created":"2020-10-28","cited_by_count":224},{"id":"1903.02351","title":"CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning","created":"2019-03-06","cited_by_count":217},{"id":"1612.08012","title":"Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge","created":"2016-12-23","cited_by_count":201},{"id":"2304.03284","title":"SegGPT: Segmenting Everything In Context","created":"2023-04-06","cited_by_count":194},{"id":"1912.01054","title":"The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge","created":"2019-12-02","cited_by_count":190},{"id":"1909.13140","title":"Feature Weighting and Boosting for Few-Shot Segmentation","created":"2019-09-28","cited_by_count":189},{"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":"2007.09886","title":"Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation","created":"2020-07-20","cited_by_count":128},{"id":"2203.09974","title":"SynthStrip: Skull-Stripping for Any Brain Image","created":"2022-03-18","cited_by_count":127},{"id":"2002.03366","title":"MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data","created":"2020-02-09","cited_by_count":121},{"id":"1904.00682","title":"Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge","created":"2019-04-01","cited_by_count":115},{"id":"2206.06694","title":"ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset","created":"2022-06-14","cited_by_count":106},{"id":"2007.05201","title":"ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model","created":"2020-07-10","cited_by_count":78},{"id":"1906.03720","title":"Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm","created":"2019-06-09","cited_by_count":77},{"id":"1902.01314","title":"'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images","created":"2019-02-04","cited_by_count":69},{"id":"1702.07841","title":"Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation","created":"2017-02-25","cited_by_count":66},{"id":"1804.02967","title":"HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation","created":"2018-04-09","cited_by_count":66},{"id":"2108.00622","title":"Recurrent Mask Refinement for Few-Shot Medical Image Segmentation","created":"2021-08-02","cited_by_count":63},{"id":"2105.07029","title":"Learning a Universal Template for Few-shot Dataset Generalization","created":"2021-05-14","cited_by_count":61},{"id":"2010.15526","title":"An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset","created":"2020-10-29","cited_by_count":59},{"id":"2107.09842","title":"Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation","created":"2021-07-21","cited_by_count":47},{"id":"2203.02048","title":"Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels","created":"2022-03-03","cited_by_count":42},{"id":"1903.03148","title":"Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation","created":"2019-03-07","cited_by_count":40},{"id":"2003.01995","title":"A Learning Strategy for Contrast-agnostic MRI Segmentation","created":"2020-03-04","cited_by_count":35},{"id":"2203.16680","title":"Learning the Effect of Registration Hyperparameters with HyperMorph","created":"2022-03-30","cited_by_count":35},{"id":"2212.03967","title":"Few-shot Medical Image Segmentation with Cycle-resemblance Attention","created":"2022-12-07","cited_by_count":33},{"id":"1801.03182","title":"BUSIS: A Benchmark for Breast Ultrasound Image Segmentation","created":"2018-01-09","cited_by_count":32},{"id":"1704.03379","title":"Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities","created":"2017-04-11","cited_by_count":25},{"id":"2208.11451","title":"Q-Net: Query-Informed Few-Shot Medical Image Segmentation","created":"2022-08-24","cited_by_count":24},{"id":"2010.02281","title":"Early Detection of Myocardial Infarction in Low-Quality Echocardiography","created":"2020-10-05","cited_by_count":21},{"id":"2008.07724","title":"Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging","created":"2020-08-18","cited_by_count":18},{"id":"2008.04615","title":"Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection","created":"2020-08-11","cited_by_count":17},{"id":"1908.05099","title":"Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation","created":"2019-08-14","cited_by_count":15},{"id":"2007.04349","title":"Deep Placental Vessel Segmentation for Fetoscopic Mosaicking","created":"2020-07-08","cited_by_count":15},{"id":"2106.03223","title":"Meta-learning with implicit gradients in a few-shot setting for medical image segmentation","created":"2021-06-06","cited_by_count":15},{"id":"2202.06498","title":"Task-Adaptive Feature Transformer with Semantic Enrichment for Few-Shot Segmentation","created":"2022-02-14","cited_by_count":5},{"id":"2208.12428","title":"Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEs","created":"2022-08-26","cited_by_count":5}],"next_cursor":null}