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Background: The need for postmastectomy breast reconstruction surgery has increased dramatically, and significant progress has been made both in implant and autologous based breast reconstruction in recent decades. In this paper, we performed a bibliometric analysis with the aim of providing an overview of the developments in breast reconstruction research and insight into the research trends. Methods: We searched the Science Citation Index Expanded database and the Web of Science Core Collection for articles published between 1991 to 2018 in the topic domain, using title, abstract, author keywords, and KeyWords Plus. Four citation indicators TCyear, Cyear, C 0 and CPP year were employed to help analyse the identified articles. Results: The number of scientific articles in breast reconstruction in this period steadily increased. It took most articles nearly a decade to hit a plateau in terms of citation counts. Plastic and Reconstructive Surgery, Annals of Plastic Surgery, and Journal of Plastic Reconstructive and Aesthetic Surgery published the largest number of articles on breast reconstruction. Nine of the top ten most prolific publications were based in the USA. The research highlights related to breast reconstruction were implant-based breast reconstruction, deep inferior epigastric perforator (DIEP) flap breast reconstruction, and superficial inferior epigastric artery (SIEA) flap breast reconstruction. Conclusions: This bibliometric analysis yielded data on citation number, publication outputs, categories, journals, institutions, countries, research highlights and tendencies. It helps to picture the panorama of breast reconstruction research, and guide the future research work.

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There are many cases that a neural-network-based system must memorize some new patterns incrementally. However, if the network learns the new patterns only by referring to them, it probably forgets old memorized patterns, since parameters in the network usually correlate not only to the old memories but also to the new patterns. A certain way to avoid the loss of memories is to learn the new patterns with all memorized patterns. It needs, however, a large computational power. To solve this problem, we propose incremental learning methods with retrieving interfered patterns (ILRI). In these methods, the system employs a modified version of a resource allocating network (RAN) which is one variation of a generalized radial basis function (GRBF). In ILRI, the RAN learns new patterns with a relearning of a few number of retrieved past patterns that are interfered with the incremental learning. In this paper, we construct ILRI in two steps. In the first step, we construct a system which searches the interfered patterns from past input patterns stored in a database. In the second step, we improve the first system in such a way that the system does not need the database. In this case, the system regenerates the input patterns approximately in a random manner. The simulation results show that these two systems have almost the same ability, and the generalization ability is higher than other similar systems using neural networks and k-nearest neighbors.

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