Dr. Kalpathy-Cramer is an Associate Professor of Radiology at Harvard Medical School, Co-Director of the QTIM lab and the Center for Machine Learning at the Athinoula A. Martinos Center and Scientific Director at the MGH & BWH Center for Clinical Data Science. Her research areas include machine learning, informatics, image analysis and statistical methods. In addition to developing novel machine learning algorithms, her lab is also actively engaged in the applications of these to clinical problems in radiology, oncology and ophthalmology.

She is funded through NIH to develop quantitative imaging methods in cancer. She is the PI of an NSF-funded project to develop novel algorithms and apply them to build diagnostic tools in ophthalmology. Research from this work has resulted in a deep-learning based algorithm for disease diagnosis and response assessment that is currently being evaluated at several clinics and screening trials in the US and India. Her group has recently applied novel machine learning methods to stroke segmentation, identification, and outcome prediction. She leads an effort to develop open-source tools for deep learning based image analysis and is making trained models for brain tumor, stroke and other diseases publicly available.


PhD in Electrical Engineering, Rensselaer Polytechnic Institute

Select Publications

1. Chang K, Beers AL, Bai HX, Brown JM, Ly KI, Li X, Senders JT, Kavouridis VK, Boaro A, Su C, Bi WL, Rapalino O, Liao W, Shen Q, Zhou H, Xiao B, Wang Y, Zhang PJ, Pinho MC, Wen PY, Batchelor TT, Boxerman JL, Arnaout O, Rosen BR, Gerstner ER, Yang L, Huang RY, Kalpathy-Cramer J. Automatic assessment of glioma burden: A deep learning algorithm for fully automated volumetric and bi-dimensional measurement. Neuro Oncol. 2019 Jun 13. pii: noz106.

2. Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kalpathy-Cramer J, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium. Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks. JAMA Ophthalmol. 2018 Jul 1;136(7):803-810.

3. Chang K, Balachandar N, Lam C, Yi D, Brown J, Beers A, Rosen B, Rubin DL, Kalpathy-Cramer J. Distributed deep learning networks among institutions for medical imaging. J Am Med Inform Assoc. 2018 Aug 1;25(8):945-954.


Open source DeepNeuro for machine learning applications in radiology

DeepROP tool for diagnosis of retinopathy of prematurity


The Quantitative Translational Imaging in Medicine Lab