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.

Methods for analyzing structures of psychological associations in a systematic way are developed. A framework is developed which shows relations among the methods. First, a directed graph representation and a cluster analysis procedure are considered for a particular application for which the data was obtained in a controlled association test. Second, the directed graph and the cluster analysis serve as a basis for developing two methods for statistical hypothesis testing. These hypothesis-testing methods are used for comparing association structures between two populations. Finally, a framework showing the relation among these four methods is used for developing a different system of methods for a free association test. These methods are applied to data obtained from association tests. A questionnaire on the living conditions of local residents in urban areas was administered to obtain this data. The aim of the questionnaire is to provide macroscopic views for decisionmaking on improvement needed for the urban environment. Several diagrams showing the association structures are given, and the significance of this family of methods is discussed.