{"list":"other","total":56,"rows":[{"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":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"2010.02502","title":"Denoising Diffusion Implicit Models","created":"2020-10-06","cited_by_count":9781},{"id":"2105.05233","title":"Diffusion Models Beat GANs on Image Synthesis","created":"2021-05-11","cited_by_count":8255},{"id":"1911.05722","title":"Momentum Contrast for Unsupervised Visual Representation Learning","created":"2019-11-13","cited_by_count":7513},{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"2207.12598","title":"Classifier-Free Diffusion Guidance","created":"2022-07-26","cited_by_count":5847},{"id":"2210.02747","title":"Flow Matching for Generative Modeling","created":"2022-10-06","cited_by_count":5702},{"id":"2203.00667","title":"Generative Adversarial Networks","created":"2022-03-01","cited_by_count":5627},{"id":"2006.07733","title":"Bootstrap your own latent: A new approach to self-supervised Learning","created":"2020-06-13","cited_by_count":4750},{"id":"1711.00937","title":"Neural Discrete Representation Learning","created":"2017-11-02","cited_by_count":4712},{"id":"1907.05600","title":"Generative Modeling by Estimating Gradients of the Data Distribution","created":"2019-07-12","cited_by_count":4101},{"id":"2102.09672","title":"Improved Denoising Diffusion Probabilistic Models","created":"2021-02-18","cited_by_count":3761},{"id":"2209.03003","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","created":"2022-09-07","cited_by_count":3700},{"id":"2206.00364","title":"Elucidating the Design Space of Diffusion-Based Generative Models","created":"2022-06-01","cited_by_count":3045},{"id":"1803.07728","title":"Unsupervised Representation Learning by Predicting Image Rotations","created":"2018-03-21","cited_by_count":1591},{"id":"1901.07031","title":"CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison","created":"2019-01-21","cited_by_count":1423},{"id":"1603.09246","title":"Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles","created":"2016-03-30","cited_by_count":1382},{"id":"2111.05826","title":"Palette: Image-to-Image Diffusion Models","created":"2021-11-10","cited_by_count":1300},{"id":"1906.02691","title":"An Introduction to Variational Autoencoders","created":"2019-06-06","cited_by_count":1000},{"id":"1910.09457","title":"Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods","created":"2019-10-21","cited_by_count":913},{"id":"2210.03142","title":"On Distillation of Guided Diffusion Models","created":"2022-10-06","cited_by_count":725},{"id":"2202.09778","title":"Pseudo Numerical Methods for Diffusion Models on Manifolds","created":"2022-02-20","cited_by_count":700},{"id":"2106.05931","title":"Score-based Generative Modeling in Latent Space","created":"2021-06-10","cited_by_count":636},{"id":"1703.05921","title":"Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery","created":"2017-03-17","cited_by_count":566},{"id":"2112.07804","title":"Tackling the Generative Learning Trilemma with Denoising Diffusion GANs","created":"2021-12-15","cited_by_count":516},{"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":"1811.08839","title":"fastMRI: An Open Dataset and Benchmarks for Accelerated MRI","created":"2018-11-21","cited_by_count":470},{"id":"2410.12557","title":"One Step Diffusion via Shortcut Models","created":"2024-10-16","cited_by_count":391},{"id":"2206.01653","title":"Metrics reloaded: Recommendations for image analysis validation","created":"2022-06-03","cited_by_count":195},{"id":"2211.13449","title":"Fast Sampling of Diffusion Models via Operator Learning","created":"2022-11-24","cited_by_count":175},{"id":"2303.09769","title":"Denoising Diffusion Autoencoders are Unified Self-supervised Learners","created":"2023-03-17","cited_by_count":130},{"id":"2310.02664","title":"On Memorization in Diffusion Models","created":"2023-10-04","cited_by_count":125},{"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":"2105.13381","title":"Recent advances and clinical applications of deep learning in medical image analysis","created":"2021-05-27","cited_by_count":104},{"id":"2308.10916","title":"Diffusion Model as Representation Learner","created":"2023-08-21","cited_by_count":95},{"id":"1907.02796","title":"Unsupervised Anomaly Localization using Variational Auto-Encoders","created":"2019-07-04","cited_by_count":73},{"id":"2307.15208","title":"Generative AI for Medical Imaging: extending the MONAI Framework","created":"2023-07-27","cited_by_count":68},{"id":"2005.00031","title":"Unsupervised Lesion Detection via Image Restoration with a Normative Prior","created":"2020-04-30","cited_by_count":65},{"id":"1812.05941","title":"Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection","created":"2018-12-14","cited_by_count":64},{"id":"2309.12325","title":"FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare","created":"2023-08-11","cited_by_count":60},{"id":"2207.12268","title":"What is Healthy? Generative Counterfactual Diffusion for Lesion Localization","created":"2022-07-25","cited_by_count":52},{"id":"2107.02622","title":"Detecting Outliers with Poisson Image Interpolation","created":"2021-07-06","cited_by_count":44},{"id":"2102.11650","title":"Unsupervised Brain Anomaly Detection and Segmentation with Transformers","created":"2021-02-23","cited_by_count":34},{"id":"2109.03812","title":"fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data","created":"2021-09-08","cited_by_count":29},{"id":"2109.06023","title":"Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI","created":"2021-09-13","cited_by_count":23},{"id":"2303.08452","title":"Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection","created":"2023-03-15","cited_by_count":18},{"id":"2410.17664","title":"Deep Generative Models for 3D Medical Image Synthesis","created":"2024-10-23","cited_by_count":14},{"id":"2403.11667","title":"Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection","created":"2024-03-18","cited_by_count":7},{"id":"2307.11654","title":"FEDD -- Fair, Efficient, and Diverse Diffusion-based Lesion Segmentation and Malignancy Classification","created":"2023-07-21","cited_by_count":4},{"id":"2308.13861","title":"Bias in Unsupervised Anomaly Detection in Brain MRI","created":"2023-08-26","cited_by_count":4},{"id":"2309.14198","title":"(Predictable) Performance Bias in Unsupervised Anomaly Detection","created":"2023-09-25","cited_by_count":4},{"id":"2404.01102","title":"Diffusion based Zero-shot Medical Image-to-Image Translation for Cross Modality Segmentation","created":"2024-04-01","cited_by_count":2},{"id":"2407.14892","title":"Latent Pollution Model: The Hidden Carbon Footprint in 3D Image Synthesis","created":"2024-07-20","cited_by_count":2}],"next_cursor":null}