Luu-Ngoc Do1, Chae Yeong Im2, Jae Hyuk Park2, So Yeon Ki3, Ilwoo Park2,4,5, and Hyo Soon Lim2,3
1Department of Radiology, Chonnam National University, Gwangju, Korea, Republic of, 2College of Medicine, Chonnam National University, Gwangju, Korea, Republic of, 3Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea, Republic of, 4Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea, Republic of, 5Department of Radiology, Chonnam National University Hospital, Gwangju, Korea, Republic of
1Department of Radiology, Chonnam National University, Gwangju, Korea, Republic of, 2College of Medicine, Chonnam National University, Gwangju, Korea, Republic of, 3Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea, Republic of, 4Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea, Republic of, 5Department of Radiology, Chonnam National University Hospital, Gwangju, Korea, Republic of
In this paper, we developed a 2-step algorithm
utilizing a recurrent CNN model and demonstrated that the proposed algorithm
can provide a method to predict invasiveness in the core needle biopsy-proven
DCIS with the results comparable to the previous reports.
Figure 1. The diagram of the proposed
two-step deep learning model.
Figure 3. ROC curves of 3 models on testing data
