Ziba Gandomkar, Ernest U. Ekpo, Sarah J. Lewis 0001, Moayyad E. Suleiman, Somphone Siviengphanom, Tong Li, Patrick C. Brennan. An end-to-end deep learning model can detect the gist of the abnormal in prior mammograms as perceived by experienced radiologists. In Frank W. Samuelson, Sian Taylor-Phillips, editors, Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, Online, February 15-19, 2021. Volume 11599 of SPIE Proceedings, SPIE, 2021. [doi]
@inproceedings{GandomkarE0SSLB21, title = {An end-to-end deep learning model can detect the gist of the abnormal in prior mammograms as perceived by experienced radiologists}, author = {Ziba Gandomkar and Ernest U. Ekpo and Sarah J. Lewis 0001 and Moayyad E. Suleiman and Somphone Siviengphanom and Tong Li and Patrick C. Brennan}, year = {2021}, doi = {10.1117/12.2582099}, url = {https://doi.org/10.1117/12.2582099}, researchr = {https://researchr.org/publication/GandomkarE0SSLB21}, cites = {0}, citedby = {0}, booktitle = {Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, Online, February 15-19, 2021}, editor = {Frank W. Samuelson and Sian Taylor-Phillips}, volume = {11599}, series = {SPIE Proceedings}, publisher = {SPIE}, isbn = {9781510640283}, }