title: Feasibility Study of a Generalized Framework for Developing Computer-Aided Detection Systems-a New Paradigm.
abstract: We propose a generalized framework for developing computer-aided detection (CADe) systems whose characteristics depend only on those of the training dataset. The purpose of this study is to show the feasibility of the framework. Two different CADe systems were experimentally developed by a prototype of the framework, but with different training datasets. The CADe systems include four components; preprocessing, candidate area extraction, candidate detection, and candidate classification. Four pretrained algorithms with dedicated optimization/setting methods corresponding to the respective components were prepared in advance. The pretrained algorithms were sequentially trained in the order of processing of the components. In this study, two different datasets, brain MRA with cerebral aneurysms and chest CT with lung nodules, were collected to develop two different types of CADe systems in the framework. The performances of the developed CADe systems were evaluated by threefold cross-validation. The CADe systems for detecting cerebral aneurysms in brain MRAs and for detecting lung nodules in chest CTs were successfully developed using the respective datasets. The framework was shown to be feasible by the successful development of the two different types of CADe systems. The feasibility of this framework shows promise for a new paradigm in the development of CADe systems: development of CADe systems without any lesion specific algorithm designing.
authors.affiliation: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. mnemo-tky@umin.net.
authors.forename: Mitsutaka
authors.identifier: http://orcid.org/0000-0003-4229-5823
authors.initials: M
authors.lastname: Nemoto
authors.affiliation: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
authors.forename: Naoto
authors.initials: N
authors.lastname: Hayashi
authors.affiliation: Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
authors.forename: Shouhei
authors.initials: S
authors.lastname: Hanaoka
authors.affiliation: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
authors.forename: Yukihiro
authors.initials: Y
authors.lastname: Nomura
authors.affiliation: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
authors.forename: Soichiro
authors.initials: S
authors.lastname: Miki
authors.affiliation: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
authors.forename: Takeharu
authors.initials: T
authors.lastname: Yoshikawa
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D002532:Intracranial Aneurysm / Q000000981:diagnostic imaging*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008297:Male; D008875:Middle Aged; D055613:Multiple Pulmonary Nodules / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Automatic optimization; CADe training dataset; Computer-aided detection (CADe) system; Generalized CADe framework; Machine learning method