Managing Computer-Assisted Detection System Based on Transfer Learning with Negative Transfer Inhibi
Issei Sato (The University of Tokyo); Yukihiro Nomura (The University of Tokyo); Shouhei Hanaoka (The University of Tokyo); Soichiro Miki (The University of Tokyo); Naoto Hayashi (The University of Tokyo); Osamu Abe (The University of Tokyo); Yoshitaka Masutani (Hiroshima City University)
The reading workload for radiologists is increasing because the numbers of examinations and images per examination are increasing due to the technical progress on imaging modalities such as computed tomography and magnetic resonance imaging. A computer-assisted detection (CAD) system based on machine learning is expected to assist radiologists. The preliminary results of a multi-institutional study indicate that the performance of the CAD system for each institution improved using training data of other institutions. This indicates that transfer learning may be useful for developing the CAD systems among multiple institutions. In this paper, we focus on transfer learning without sharing training data due to the need to protect personal information in each institution. Moreover, we raise a problem of negative transfer in CAD system and propose an algorithm for inhibiting negative transfer. Our algorithm provides a theoretical guarantee for managing CAD software in terms of transfer learning and exhibits experimentally better performance compared to that of the current algorithm in cerebral aneurysm detection.