Ken Chang
Harvard University
111 Papers
622 Citations
Ken Chang is an academic researcher from Harvard University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 23, co-authored 96 publications. Previous affiliations of Ken Chang include National Institutes of Health & Brigham and Women's Hospital.
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Papers
Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement.
Ken Chang,Andrew Beers,Harrison X. Bai,James M. Brown,K. Ina Ly,Xuejun Li,Joeky T. Senders,Vasileios K. Kavouridis,Alessandro Boaro,Chang Su,Wenya Linda Bi,Otto Rapalino,Weihua Liao,Qin Shen,Hao Zhou,Bo Xiao,Yinyan Wang,Paul J. Zhang,Marco C. Pinho,Patrick Y. Wen,Tracy T. Batchelor,Jerrold L. Boxerman,Omar Arnaout,Bruce R. Rosen,Elizabeth R. Gerstner,Li Yang,Raymond Y. Huang,Jayashree Kalpathy-Cramer +27 more
TL;DR: A deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the RANO criteria demonstrates potential utility for evaluating tumor burden in complex posttreatment settings.
169
Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging
Nishanth Thumbavanam Arun,Nathan Gaw,Praveer Singh,Ken Chang,Mehak Aggarwal,Bryan Chen,Katharina Hoebel,Sharut Gupta,Jay B. Patel,Mishka Gidwani,Julius Adebayo,Matthew D. Li,Jayashree Kalpathy-Cramer +12 more
- 06 Oct 2021
TL;DR: A variety of saliency map techniques used to interpret deep neural networks trained on medical imaging did not pass several key criteria for utility and robustness, highlighting the need for additi... as mentioned in this paper.
166
•Posted Content
Split Learning for collaborative deep learning in healthcare
Maarten G. Poirot,Praneeth Vepakomma,Ken Chang,Jayashree Kalpathy-Cramer,Rajiv Gupta,Ramesh Raskar +5 more
TL;DR: This work proves the significant benefit of distributed learning in healthcare, and paves the way for future real-world implementations of split learning based approach in the medical field.
131
Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas
Hao Zhou,Ken Chang,Harrison X. Bai,Bo Xiao,Chang Su,Wenya Linda Bi,Paul J. Zhang,Joeky T. Senders,Martin Vallières,Vasileios K. Kavouridis,Alessandro Boaro,Omar Arnaout,Li Yang,Raymond Y. Huang +13 more
TL;DR: Using machine-learning algorithms, high accuracy was achieved in the prediction of IDH genotype in gliomas and moderate accuracy in a three-group prediction including IDh genotype and 1p19q codeletion.