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Medical data poses a daunting challenge for AI algorithms: it exists in many different modalities, experiences frequent distribution shifts, and suffers from a scarcity of examples and labels.
The sleep heart health study: design, rationale, and methods
S. F. Quan, B. V. Howard, C. Iber, J. P. Kiley, F. J. Nieto, G. T. O’Connor, D. M. Rapoport, S. Redline, J. Robbins, J. M. Samet, et al · 1997
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Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem
I. of Medicine · 2006
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The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans
S. G. Armato III, G. McLennan, L. Bidaut, M. F. McNitt-Gray, C. R. Meyer, A. P. Reeves, B. Zhao, D. R. Aberle, C. I. Henschke, E. A. Hoffman, et al · 2011
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Automated analysis of retinal images for detection of referable diabetic retinopathy
M. D. Abràmoff, J. C. Folk, D. P. Han, J. D. Walker, D. F. Williams, S. R. Russell, P. Massin, B. Cochener, P. Gain, L. Tang, et al · 2013
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The cancer imaging archive (TCIA): maintaining and operating a public information repository
K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, S. Phillips, D. Maffitt, M. Pringle, L. Tarbox, and F. Prior · 2013
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Medical Imaging: Essentials for Physicians
A. B. Wolbarst, P. Capasso, and A. R. Wyant · 2013
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Feedback on a publicly distributed image database: the messidor database
E. Decencière, X. Zhang, G. Cazuguel, B. Lay, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, et al · 2014
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The AASM recommended and acceptable EEG montages are comparable for the staging of sleep and scoring of EEG arousals
B. Duce, C. Rego, J. Milosavljevic, and C. Hukins · 2014
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Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning
M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer · 2016
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Isruc-sleep: A comprehensive public dataset for sleep researchers
S. Khalighi, T. Sousa, J. M. Santos, and U. Nunes · 2016
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Trends in sleep studies performed for medicare beneficiaries
W. Chiao and M. L. Durr · 2017
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A curated mammography data set for use in computer-aided detection and diagnosis research
R. S. Lee, F. Gimenez, A. Hoogi, K. K. Miyake, M. Gorovoy, and D. L. Rubin · 2017
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Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy
H. Takahashi, H. Tampo, Y. Arai, Y. Inoue, and H. Kawashima · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
P. Tschandl, C. Rosendahl, and H. Kittler · 2018
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Bcn20000: Dermoscopic lesions in the wild
M. Combalia, N. C. Codella, V. Rotemberg, B. Helba, V. Vilaplana, O. Reiter, C. Carrera, A. Barreiro, A. C. Halpern, S. Puig, et al · 2019
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Value of dermoscopy in a Population-Based screening sample by dermatologists
I. Hoorens, K. Vossaert, S. Lanssens, L. Dierckxsens, G. Argenziano, and L. Brochez · 2019
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
J. Irvin, P. Rajpurkar, M. Ko, Y. Yu, S. Ciurea-Ilcus, C. Chute, H. Marklund, B. Haghgoo, R. Ball, K. Shpanskaya, et al · 2019
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Lndb: a lung nodule database on computed tomography
J. Pedrosa, G. Aresta, C. Ferreira, M. Rodrigues, P. Leitão, A. S. Carvalho, J. Rebelo, E. Negrão, I. Ramos, A. Cunha, et al · 2019
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A large-scale study of representation learning with the visual task adaptation benchmark
X. Zhai, J. Puigcerver, A. Kolesnikov, P. Ruyssen, C. Riquelme, M. Lucic, J. Djolonga, A. S. Pinto, M. Neumann, A. Dosovitskiy, L. Beyer, O. Bachem, M. Tschannen, M. Michalski, O. Bousquet, S. Gelly, and N. Houlsby · 2019
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A new Computer-Aided diagnosis system with modified genetic feature selection for BI-RADS classification of breast masses in mammograms
S. Boumaraf, X. Liu, C. Ferkous, and X. Ma · 2020
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An open-access long-term wearable ecg database for premature ventricular contractions and supraventricular premature beat detection
Z. Cai, C. Liu, H. Gao, X. Wang, L. Zhao, Q. Shen, E. Ng, and J. Li · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al · 2020
Cited alongside, same era.
i-mix: A Domain-Agnostic strategy for contrastive representation learning
K. Lee, Y. Zhu, K. Sohn, C.-L. Li, J. Shin, and H. Lee · 2020
Cited alongside, same era.
Pad-ufes-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones
A. G. Pacheco, G. R. Lima, A. S. Salomão, B. Krohling, I. P. Biral, G. G. de Angelo, F. C. Alves Jr, J. G. Esgario, A. C. Simora, P. B. Castro, et al · 2020
Cited alongside, same era.
A deep learning approach for diabetic retinopathy detection using transfer learning
Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?
A. Schreuder, E. T. Scholten, B. van Ginneken, and C. Jacobs · 2021
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Dabs: A domain-agnostic benchmark for self-supervised learning
A. Tamkin, V. Liu, R. Lu, D. E. Fein, C. Schultz, and N. D. Goodman · 2021
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Models genesis
Z. Zhou, V. Sodha, J. Pang, M. B. Gotway, and J. Liang · 2021
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Flamingo: a visual language model for few-shot learning
J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y. Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynolds, R. Ring, E. Rutherford, S. Cabi, T. Han, Z. Gong, S. Samangooei, M. Monteiro, J. L. Menick, S. Borgeaud, A. Brock, A. Nematzadeh, S. Sharifzadeh, M. a. Bińkowski, R. Barreira, O. Vinyals, A. Zisserman, and K. Simonyan · 2022
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APTOS 2019 blindness detection
APTOS 2019 Blindness Detection · 2022
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Ramchandre, B. Patil, S. Pharande, K. Javali, and H. Pande · 2020
Cited alongside, same era.
Deep learning for automated sleep staging using instantaneous heart rate
N. Sridhar, A. Shoeb, P. Stephens, A. Kharbouch, D. B. Shimol, J. Burkart, A. Ghoreyshi, and L. Myers · 2020
Cited alongside, same era.
Viewmaker networks: Learning views for unsupervised representation learning
A. Tamkin, M. Wu, and N. Goodman · 2020
Cited alongside, same era.
Ptb-xl, a large publicly available electrocardiography dataset
P. Wagner, N. Strodthoff, R.-D. Bousseljot, D. Kreiseler, F. I. Lunze, W. Samek, and T. Schaeffter · 2020
Cited alongside, same era.
A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients
J. Zheng, J. Zhang, S. Danioko, H. Yao, H. Guo, and C. Rakovski · 2020
Cited alongside, same era.
Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study
H. Zhu, C. Cheng, H. Yin, X. Li, P. Zuo, J. Ding, F. Lin, J. Wang, B. Zhou, Y. Li, S. Hu, Y. Xiong, B. Wang, G. Wan, X. Yang, and Y. Yuan · 2020
Cited alongside, same era.
What will it take to fix benchmarking in natural language understanding?
S. R. Bowman and G. E. Dahl · 2021
Cited alongside, same era.
Masked autoencoders are scalable vision learners
K. He, X. Chen, S. Xie, Y. Li, P. Doll’ar, and R. B. Girshick · 2021
Cited alongside, same era.
Robust and efficient medical imaging with Self-Supervision
S. Azizi, L. Culp, J. Freyberg, B. Mustafa, S. Baur, S. Kornblith, T. Chen, P. MacWilliams, S. Sara Mahdavi, E. Wulczyn, B. Babenko, M. Wilson, A. Loh, P.-H. C. Chen, Y. Liu, P. Bavishi, S. M. McKinney, J. Winkens, A. G. Roy, Z. Beaver, F. Ryan, J. Krogue, M. Etemadi, U. Telang, Y. Liu, L. Peng, G. S. Corrado, D. R. Webster, D. Fleet, G. Hinton, N. Houlsby, A. Karthikesalingam, M. Norouzi, and V. Natarajan · 2022
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Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective
A. Bandyopadhyay and C. Goldstein · 2022
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Benchmark datasets driving artificial intelligence development fail to capture the needs of medical professionals
K. Blagec, J. Kraiger, W. Frühwirt, and M. Samwald · 2022
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Self-Supervised representation learning: Introduction, advances, and challenges
L. Ericsson, H. Gouk, C. C. Loy, and T. M. Hospedales · 2022
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Self-supervised learning from 100 million medical images
F. C. Ghesu, B. Georgescu, A. Mansoor, Y. Yoo, D. Neumann, P. Patel, R. S. Vishwanath, J. M. Balter, Y. Cao, S. Grbic, and D. Comaniciu · 2022
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OmniMAE: Single model masked pretraining on images and videos
R. Girdhar, A. El-Nouby, M. Singh, K. V. Alwala, A. Joulin, and I. Misra · 2022
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Self-supervised learning in medicine and healthcare
R. Krishnan, P. Rajpurkar, and E. J. Topol · 2022
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Mapping global dynamics of benchmark creation and saturation in artificial intelligence
S. Ott, A. Barbosa-Silva, K. Blagec, J. Brauner, and M. Samwald · 2022
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Curated breast imaging subset of digital database for screening mammography (CBIS-DDSM) - the cancer imaging archive (TCIA) public access - cancer imaging archive wiki
K. Smith · 2022
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Dabs 2.0: Improved datasets and algorithms for universal self-supervision
A. Tamkin, G. Banerjee, M. Owda, V. Liu, S. Rammoorthy, and N. Goodman · 2022
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The 4th asia pacific Tele-Ophthalmology society symposium
The 4th Asia Pacific Tele-Ophthalmology Society Symposium · 2022
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PTB-XL, a large publicly available electrocardiography dataset, Nov. 2022
P. Wagner, N. Strodthoff, R.-D. Bousseljot, W. Samek, and T. Schaeffter · 2022
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Wild-Time: A benchmark of in-the-wild distribution shift over time
H. Yao, C. Choi, B. Cao, Y. Lee, P. W. Koh, and C. Finn · 2022
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Self pre-training with masked autoencoders for medical image analysis
L. Zhou, H. Liu, J. Bae, J. He, D. Samaras, and P. Prasanna · 2022
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GPT-4 technical report
OpenAI · 2023
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