Connection
Jayashree Kalpathy-Cramer to Mammography
This is a "connection" page, showing publications Jayashree Kalpathy-Cramer has written about Mammography.
|
|
| |
Connection Strength |
|
 |
|
 |
| |
0.566 |
|
|
|
-
Brink L, Romero RA, Coombs L, Tilkin M, Mazaheri S, Gichoya J, Zaiman Z, Trivedi H, Medina A, Bizzo BC, Chang K, Kalpathy-Cramer J, Kalra MK, Astuto B, Ramirez C, Majumdar S, Lee AY, Lee CI, Cross NM, Chen PH, Ciancibello M, Chiunda A, Nachand D, Shah C, Wald C. Multi-Institutional Evaluation and Training of Breast Density Classification AI Algorithm Using ACR Connect and AI-LAB. J Am Coll Radiol. 2025 02; 22(2):211-219.
Score: 0.193
-
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 07; 95:103206.
Score: 0.187
-
Chang K, Beers AL, Brink L, Patel JB, Singh P, Arun NT, Hoebel KV, Gaw N, Shah M, Pisano ED, Tilkin M, Coombs LP, Dreyer KJ, Allen B, Agarwal S, Kalpathy-Cramer J. Multi-Institutional Assessment and Crowdsourcing Evaluation of Deep Learning for Automated Classification of Breast Density. J Am Coll Radiol. 2020 Dec; 17(12):1653-1662.
Score: 0.143
-
Konz N, Buda M, Gu H, Saha A, Yang J, Chledowski J, Park J, Witowski J, Geras KJ, Shoshan Y, Gilboa-Solomon F, Khapun D, Ratner V, Barkan E, Ozery-Flato M, Martí R, Omigbodun A, Marasinou C, Nakhaei N, Hsu W, Sahu P, Hossain MB, Lee J, Santos C, Przelaskowski A, Kalpathy-Cramer J, Bearce B, Cha K, Farahani K, Petrick N, Hadjiiski L, Drukker K, Armato SG, Mazurowski MA. A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis. JAMA Netw Open. 2023 02 01; 6(2):e230524.
Score: 0.043
|
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.
|