Regular articleMachine learning, computational pathology, and biophysical imagingDeep-Learning–Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies
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Supported by the Karen Toffler Charitable Trust grant (V.B.K.), the National Center for Advancing Translational Sciences grant (V.B.K.), the NIH, through Boston University Clinical & Translational Science Institute (BU-CTSI) grant 1UL1TR001430 (V.B.K.), a Scientist Development grant 17SDG33670323 (V.B.K.), an American Heart Association Strategically Focused Research Network Center grant 20SFRN35460031 (V.B.K.), a Hariri Research Award from the Hariri Institute for Computing and Computational Science and Engineering at Boston University (V.B.K.), and NIH grants R01-AG062109 and R21-CA253498 (V.B.K.); NIH grants R21-DK119740 and R01-HL132325 (V.C.C.); the German Research Foundation (DFG) SFB/TRR57 P25&P33, SFB/TRR219 Project-ID 322900939, BO3755/13-1 Project-ID 454024652 (P.B.), the German Federal Ministries of Education and Research STOP-FSGS-01GM1901A (P.B.), and Health (DEEP LIVER, ZMVI1-2520DAT111) and Economic Affairs and Energy (EMPAIA) (P.B.), the European Research Council (ERC) AIM.imaging.CKD No. 101001791 (P.B.); NIH grants R21-DK119751 and U01-DK085660 (S.S.W.); and National Science Foundation grants 1838193 and 1551572 (M.B.).
Disclosures: None declared.
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