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Language-supervised pre-training has proven to be a valuable method for extracting semantically meaningful features from images, serving as a foundational element in multimodal systems within the computer vision and medical imaging domains.
Do lateral views help automated chest x-ray predictions?
Hadrien Bertrand, Mohammad Hashir, and Joseph Paul Cohen · 1904
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 1905
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Signature verification using a "siamese" time delay neural network
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Physiobank, physiotoolkit, and physionet
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Scikit-learn: Machine learning in Python
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U-net: Convolutional networks for biomedical image segmentation
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Preparing a collection of radiology examinations for distribution and retrieval
Dina Demner-Fushman, Marc D Kohli, Marc B Rosenman, Sonya E Shooshan, Laritza Rodriguez, Sameer Antani, George R Thoma, and Clement J McDonald · 2016
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Does body mass index outperform body weight as a surrogate parameter in the calculation of size-specific dose estimates in adult body ct?
Johannes Boos, Rotem S Lanzman, Philipp Heusch, Joel Aissa, Christoph Schleich, Christoph Thomas, Lino M Sawicki, Gerald Antoch, and Patric Kröpil · 2016
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers · 2017
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Unified perceptual parsing for scene understanding
Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and Jian Sun · 2018
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Rsna pneumonia detection challenge, 2018
Anouk Stein MD, Carol Wu, Chris Carr, George Shih, Jamie Dulkowski, kalpathy, Leon Chen, Luciano Prevedello, Marc Kohli MD, Mark McDonald, Peter, Phil Culliton, Safwan Halabi MD, and Tian Xia · 2018
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nnu-net: Self-adapting framework for u-net-based medical image segmentation
Fabian Isensee, Jens Petersen, Andre Klein, David Zimmerer, Paul F. Jaeger, Simon Kohl, Jakob Wasserthal, Gregor Koehler, Tobias Norajitra, Sebastian Wirkert, and Klaus H. Maier-Hein · 2018
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Spreading vectors for similarity search
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, and Hervé Jégou · 2018
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MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports
Alistair EW Johnson, Tom J Pollard, Seth J Berkowitz, Nathaniel R Greenbaum, Matthew P Lungren, Chih-ying Deng, Roger G Mark, and Steven Horng · 2019
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Models genesis: Generic autodidactic models for 3d medical image analysis
Zongwei Zhou, Vatsal Sodha, Md Mahfuzur Rahman Siddiquee, Ruibin Feng, Nima Tajbakhsh, Michael B Gotway, and Jianming Liang · 2019
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CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn L. Ball, Katie Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, and Andrew Y. Ng · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Quantifying the value of lateral views in deep learning for chest x-rays
Mohammad Hashir, Hadrien Bertrand, and Joseph Paul Cohen · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Combining automatic labelers and expert annotations for accurate radiology report labeling using BERT
Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Ng, and Matthew Lungren · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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PadChest: A large chest x-ray image dataset with multi-label annotated reports
Aurelia Bustos, Antonio Pertusa, Jose-Maria Salinas, and Maria de la Iglesia-Vayá · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Exploring large-scale public medical image datasets
Luke Oakden-Rayner · 2020
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Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation
Anna Majkowska, Sid Mittal, David F Steiner, Joshua J Reicher, Scott Mayer McKinney, Gavin E Duggan, Krish Eswaran, Po-Hsuan Cameron Chen, Yun Liu, Sreenivasa Raju Kalidindi, et al · 2020
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The effect of image resolution on deep learning in radiography
Carl F Sabottke and Bradley M Spieler · 2020
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Virtex: Learning visual representations from textual annotations
Karan Desai and Justin Johnson · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition
Shih-Cheng Huang, Liyue Shen, Matthew P Lungren, and Serena Yeung · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Curation of the candid-ptx dataset with free-text reports
Sijing Feng, Damian Azzollini, Ji Soo Kim, Cheng-Kai Jin, Simon P Gordon, Jason Yeoh, Eve Kim, Mina Han, Andrew Lee, Aakash Patel, et al · 2021
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Supervised transfer learning at scale for medical imaging
Basil Mustafa, Aaron Loh, Jan Freyberg, Patricia MacWilliams, Megan Wilson, Scott Mayer McKinney, Marcin Sieniek, Jim Winkens, Yuan Liu, Peggy Bui, et al · 2021
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Chest imagenome dataset (version 1.0. 0)
Joy T Wu, Nkechinyere N Agu, Ismini Lourentzou, Arjun Sharma, Joseph A Paguio, Jasper S Yao, Edward C Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, et al · 2021
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Beit: Bert pre-training of image transformers
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei · 2021
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Vindr-ribcxr: A benchmark dataset for automatic segmentation and labeling of individual ribs on chest x-rays, 2021
Hoang C. Nguyen, Tung T. Le, Hieu H. Pham, and Ha Q. Nguyen · 2021
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A deep-learning method using computed tomography scout images for estimating patient body weight
Shota Ichikawa, Misaki Hamada, and Hiroyuki Sugimori · 2021
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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Filip: Fine-grained interactive language-image pre-training
Confounders mediate AI prediction of demographics in medical imaging
Grant Duffy, Shoa L. Clarke, Matthew Christensen, Bryan He, Neal Yuan, Susan Cheng, and David Ouyang · 2022
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Advancing radiograph representation learning with masked record modeling
Hong-Yu Zhou, Chenyu Lian, Liansheng Wang, and Yizhou Yu · 2023
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Llava-med: Training a large language-and-vision assistant for biomedicine in one day
Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, and Jianfeng Gao · 2023
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Towards generalist biomedical ai
Tao Tu, Shekoofeh Azizi, Danny Driess, Mike Schaekermann, Mohamed Amin, Pi-Chuan Chang, Andrew Carroll, Chuck Lau, Ryutaro Tanno, Ira Ktena, et al · 2023
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Scaling vision transformers to 22 billion parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, et al · 2023
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Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu · 2021
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Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm
Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan · 2021
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Big self-supervised models advance medical image classification
Shekoofeh Azizi, Basil Mustafa, Fiona Ryan, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith, Ting Chen, et al · 2021
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Deep learning for chest x-ray analysis: A survey
Erdi Çallı, Ecem Sogancioglu, Bram van Ginneken, Kicky G van Leeuwen, and Keelin Murphy · 2021
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Pneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training
Johannes Rueckel, Christian Huemmer, Andreas Fieselmann, Florin-Cristian Ghesu, Awais Mansoor, Balthasar Schachtner, Philipp Wesp, Lena Trappmann, Basel Munawwar, Jens Ricke, et al · 2021
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Retrieval-based chest x-ray report generation using a pre-trained contrastive language-image model
Mark Endo, Rayan Krishnan, Viswesh Krishna, Andrew Y Ng, and Pranav Rajpurkar · 2021
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Improving factual completeness and consistency of image-to-text radiology report generation
Yasuhide Miura, Yuhao Zhang, Emily Tsai, Curtis Langlotz, and Dan Jurafsky · 2021
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The future of ai and informatics in radiology: 10 predictions, 2023
Curtis P Langlotz · 2023
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Contrastive masked autoencoders are stronger vision learners
Zhicheng Huang, Xiaojie Jin, Chengze Lu, Qibin Hou, Ming-Ming Cheng, Dongmei Fu, Xiaohui Shen, and Jiashi Feng · 2023
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What do self-supervised vision transformers learn?
Namuk Park, Wonjae Kim, Byeongho Heo, Taekyung Kim, and Sangdoo Yun · 2023
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Objectives matter: Understanding the impact of self-supervised objectives on vision transformer representations
Shashank Shekhar, Florian Bordes, Pascal Vincent, and Ari S Morcos · 2023
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Self-supervised learning from images with a joint-embedding predictive architecture
Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas · 2023
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Learning to exploit temporal structure for biomedical vision-language processing
Shruthi Bannur, Stephanie Hyland, Qianchu Liu, Fernando Perez-Garcia, Maximilian Ilse, Daniel C Castro, Benedikt Boecking, Harshita Sharma, Kenza Bouzid, Anja Thieme, et al · 2023
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On data scaling in masked image modeling
Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Yixuan Wei, Qi Dai, and Han Hu · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing · 2023
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Data augmentation for radiology report simplification
Ziyu Yang, Santhosh Cherian, and Slobodan Vucetic · 2023
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A fully connected reproducible se-uresnet for multiorgan chest radiographs segmentation
Debojyoti Pal, Tanushree Meena, and Sudipta Roy · 2023
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Semi-supervised multi-structure segmentation in chest x-ray imaging
Ricardo Coimbra Brioso, João Pedrosa, Ana Maria Mendonça, and Aurélio Campilho · 2023
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Determining body height and weight from thoracic and abdominal ct localizers in pediatric and young adult patients using deep learning
Aydin Demircioğlu, Anton S Quinsten, Lale Umutlu, Michael Forsting, Kai Nassenstein, and Denise Bos · 2023
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Mimic-iv, 2023
Alistair Johnson, Lucas Bulgarelli, Tom Pollard, Steven Horng, Leo Anthony Celi, and Roger Mark · 2023
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Med-unic: Unifying cross-lingual medical vision-language pre-training by diminishing bias
Zhongwei Wan, Che Liu, Mi Zhang, Jie Fu, Benyou Wang, Sibo Cheng, Lei Ma, Cesar C’esar Quilodr’an-Casas, and Rossella Arcucci · 2023
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Can generalist foundation models outcompete special-purpose tuning? case study in medicine
Harsha Nori, Yin Tat Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolo Fusi, Nicholas King, Jonathan Larson, Yuanzhi Li, Weishung Liu, et al · 2023
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From clip to dino: Visual encoders shout in multi-modal large language models, 2023
Dongsheng Jiang, Yuchen Liu, Songlin Liu, Xiaopeng Zhang, Jin Li, Hongkai Xiong, and Qi Tian · 2023
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Imagebind: One embedding space to bind them all
Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra · 2023
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Image captioners are scalable vision learners too
Michael Tschannen, Manoj Kumar, Andreas Steiner, Xiaohua Zhai, Neil Houlsby, and Lucas Beyer · 2023
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Masked autoencoding does not help natural language supervision at scale
Floris Weers, Vaishaal Shankar, Angelos Katharopoulos, Yinfei Yang, and Tom Gunter · 2023
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Medklip: Medical knowledge enhanced language-image pre-training for x-ray diagnosis
Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie · 2023
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Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
Shekoofeh Azizi, Laura Culp, Jan Freyberg, Basil Mustafa, Sebastien Baur, Simon Kornblith, Ting Chen, Nenad Tomasev, Jovana Mitrović, Patricia Strachan, et al · 2023
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Masked image modeling advances 3d medical image analysis
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Towards foundation models learned from anatomy in medical imaging via self-supervision
Mohammad Reza Hosseinzadeh Taher, Michael B Gotway, and Jianming Liang · 2023
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Scaling self-supervised learning for histopathology with masked image modeling
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Virchow: A million-slide digital pathology foundation model
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Chexmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images
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Effect of image resolution on automated classification of chest x-rays
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DINOv2: Learning robust visual features without supervision
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