@inproceedings{oai:pu-hiroshima.repo.nii.ac.jp:00001444,
 author = {市村, 匠 and ICHIMURA, Takumi and 鎌田, 真 and KAMADA, Shin},
 book = {2018 IEEE SMC Hiroshima Chapter若手研究会講演論文集},
 month = {},
 note = {application/pdf, Abstract?Deep Learning has a hierarchical network architecture to represent the complicated feature of in-put patterns. We have developed the adaptive structure learning method of Deep Belief Network (DBN) that can discover an optimal number of hidden neurons for given input data in a Restricted Boltzmann Machine (RBM) by neuron generation-annihilation algorithm, and hidden layers in DBN. We examined to the learning method to medical open database: CXR8. The CXR8 is one of the most commonly accessible radiological examination for screening and diagnosis of many lung diseases. This paper describes our method accuracy of the classi?cation and localization for the given bounding box(B-Box). The classi?cation ratio for 8 diseases were almost 100% score. A new localization method for DBN is proposed here and the discrete heatmap, the likelihood map of pathologies, was automatically constructed., 開催日：平成30年7月28日　会場：広島工業大学},
 pages = {77--83},
 publisher = {IEEE SMC Hiroshima Chapter},
 title = {ChestX-ray8を用いた構造適応型Deep Belief Networkにおける胸部疾患の分類と位置検出の試み},
 year = {2018},
 yomi = {イチムラ, タクミ and カマダ, シン}
}