 Connection
Jayashree Kalpathy-Cramer to Machine Learning
This is a "connection" page, showing publications Jayashree Kalpathy-Cramer has written about Machine Learning.
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Connection Strength |
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2.228 |
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Gidwani M, Chang K, Patel JB, Hoebel KV, Ahmed SR, Singh P, Fuller CD, Kalpathy-Cramer J. Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models. Radiology. 2023 04; 307(1):e220715.
Score: 0.545
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Hallaj S, Chuter BG, Lieu AC, Singh P, Kalpathy-Cramer J, Xu BY, Christopher M, Zangwill LM, Weinreb RN, Baxter SL. Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives. Ophthalmol Glaucoma. 2025 Jan-Feb; 8(1):92-105.
Score: 0.153
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Yang E, Li MD, Raghavan S, Deng F, Lang M, Succi MD, Huang AJ, Kalpathy-Cramer J. Transformer versus traditional natural language processing: how much data is enough for automated radiology report classification? Br J Radiol. 2023 Sep; 96(1149):20220769.
Score: 0.140
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Gupta S, Kumar S, Chang K, Lu C, Singh P, Kalpathy-Cramer J. Collaborative Privacy-preserving Approaches for Distributed Deep Learning Using Multi-Institutional Data. Radiographics. 2023 04; 43(4):e220107.
Score: 0.139
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Robinson-Weiss C, Patel J, Bizzo BC, Glazer DI, Bridge CP, Andriole KP, Dabiri B, Chin JK, Dreyer K, Kalpathy-Cramer J, Mayo-Smith WW. Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT. Radiology. 2023 Feb; 306(2):e220101.
Score: 0.134
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Li MD, Lang M, Deng F, Chang K, Buch K, Rincon S, Mehan WA, Leslie-Mazwi TM, Kalpathy-Cramer J. Analysis of Stroke Detection during the COVID-19 Pandemic Using Natural Language Processing of Radiology Reports. AJNR Am J Neuroradiol. 2021 03; 42(3):429-434.
Score: 0.119
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Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to Machine Learning, Neural Networks, and Deep Learning. Transl Vis Sci Technol. 2020 02 27; 9(2):14.
Score: 0.112
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Silva MA, Patel J, Kavouridis V, Gallerani T, Beers A, Chang K, Hoebel KV, Brown J, See AP, Gormley WB, Aziz-Sultan MA, Kalpathy-Cramer J, Arnaout O, Patel NJ. Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture. World Neurosurg. 2019 Nov; 131:e46-e51.
Score: 0.107
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Halabi SS, Prevedello LM, Kalpathy-Cramer J, Mamonov AB, Bilbily A, Cicero M, Pan I, Pereira LA, Sousa RT, Abdala N, Kitamura FC, Thodberg HH, Chen L, Shih G, Andriole K, Kohli MD, Erickson BJ, Flanders AE. The RSNA Pediatric Bone Age Machine Learning Challenge. Radiology. 2019 02; 290(2):498-503.
Score: 0.103
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Lee A, Taylor P, Kalpathy-Cramer J, Tufail A. Reply. Ophthalmology. 2018 12; 125(12):e86.
Score: 0.102
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Lee A, Taylor P, Kalpathy-Cramer J, Tufail A. Machine Learning Has Arrived! Ophthalmology. 2017 12; 124(12):1726-1728.
Score: 0.096
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Zhou M, Scott J, Chaudhury B, Hall L, Goldgof D, Yeom KW, Iv M, Ou Y, Kalpathy-Cramer J, Napel S, Gillies R, Gevaert O, Gatenby R. Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches. AJNR Am J Neuroradiol. 2018 02; 39(2):208-216.
Score: 0.095
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Bolón-Canedo V, Ataer-Cansizoglu E, Erdogmus D, Kalpathy-Cramer J, Fontenla-Romero O, Alonso-Betanzos A, Chiang MF. Dealing with inter-expert variability in retinopathy of prematurity: A machine learning approach. Comput Methods Programs Biomed. 2015 Oct; 122(1):1-15.
Score: 0.081
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Ataer-Cansizoglu E, Kalpathy-Cramer J, You S, Keck K, Erdogmus D, Chiang MF. Analysis of underlying causes of inter-expert disagreement in retinopathy of prematurity diagnosis. Application of machine learning principles. Methods Inf Med. 2015; 54(1):93-102.
Score: 0.078
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Schmidt K, Bearce B, Chang K, Coombs L, Farahani K, Elbatel M, Mouheb K, Marti R, Zhang R, Zhang Y, Wang Y, Hu Y, Ying H, Xu Y, Testagrose C, Demirer M, Gupta V, Akünal Ü, Bujotzek M, Maier-Hein KH, Qin Y, Li X, Kalpathy-Cramer J, Roth HR. Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge. Med Image Anal. 2024 Jul; 95:103206.
Score: 0.038
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Cox M, Panagides JC, Tabari A, Kalva S, Kalpathy-Cramer J, Daye D. Risk stratification with explainable machine learning for 30-day procedure-related mortality and 30-day unplanned readmission in patients with peripheral arterial disease. PLoS One. 2022; 17(11):e0277507.
Score: 0.034
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Bridge CP, Gorman C, Pieper S, Doyle SW, Lennerz JK, Kalpathy-Cramer J, Clunie DA, Fedorov AY, Herrmann MD. Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology. J Digit Imaging. 2022 12; 35(6):1719-1737.
Score: 0.033
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Cox M, Reid N, Panagides JC, Di Capua J, DeCarlo C, Dua A, Kalva S, Kalpathy-Cramer J, Daye D. Interpretable Machine Learning for the Prediction of Amputation Risk Following Lower Extremity Infrainguinal Endovascular Interventions for Peripheral Arterial Disease. Cardiovasc Intervent Radiol. 2022 May; 45(5):633-640.
Score: 0.032
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Li MD, Ahmed SR, Choy E, Lozano-Calderon SA, Kalpathy-Cramer J, Chang CY. Artificial intelligence applied to musculoskeletal oncology: a systematic review. Skeletal Radiol. 2022 Feb; 51(2):245-256.
Score: 0.031
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Daye D, Tabari A, Kim H, Chang K, Kamran SC, Hong TS, Kalpathy-Cramer J, Gee MS. Quantitative tumor heterogeneity MRI profiling improves machine learning-based prognostication in patients with metastatic colon cancer. Eur Radiol. 2021 Aug; 31(8):5759-5767.
Score: 0.030
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Langlotz CP, Allen B, Erickson BJ, Kalpathy-Cramer J, Bigelow K, Cook TS, Flanders AE, Lungren MP, Mendelson DS, Rudie JD, Wang G, Kandarpa K. A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop. Radiology. 2019 06; 291(3):781-791.
Score: 0.026
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Connection Strength
The connection strength for concepts is the sum of the scores for each matching publication.
Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
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