Pub Date : 2023-12-01DOI: 10.1587/transinf.2023edp7025
Jianbo Wang, Haozhi Huang, Li Shen, Xuan Wang, Toshihiko Yamasaki
{"title":"Hierarchical Detailed Intermediate Supervision for Image-to-Image Translation","authors":"Jianbo Wang, Haozhi Huang, Li Shen, Xuan Wang, Toshihiko Yamasaki","doi":"10.1587/transinf.2023edp7025","DOIUrl":"https://doi.org/10.1587/transinf.2023edp7025","url":null,"abstract":"","PeriodicalId":48824,"journal":{"name":"IEICE Transactions on Information and Systems","volume":"15 10","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138625059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1587/transinf.2023edl8034
SenSong He, Ying Qiu
{"title":"Energy-Efficient One-to-One and Many-to-One Concurrent Transmission for Wireless Sensor Networks","authors":"SenSong He, Ying Qiu","doi":"10.1587/transinf.2023edl8034","DOIUrl":"https://doi.org/10.1587/transinf.2023edl8034","url":null,"abstract":"","PeriodicalId":48824,"journal":{"name":"IEICE Transactions on Information and Systems","volume":" 4","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138612093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Transactional TF: Transform Library with Concurrency and Correctness","authors":"Yushi Ogiwara, Ayanori Yorozu, Akihisa Ohya, Hideyuki Kawashima","doi":"10.1587/transinf.2023pap0006","DOIUrl":"https://doi.org/10.1587/transinf.2023pap0006","url":null,"abstract":"","PeriodicalId":48824,"journal":{"name":"IEICE Transactions on Information and Systems","volume":" 21","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138614757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1587/transinf.2023pap0009
Kensuke Iizuka, Haruna Takagi, Aika Kamei, Kazuei Hironaka, Hideharu Amano
{"title":"Power Analysis and Power Modeling of Directly-Connected FPGA Clusters","authors":"Kensuke Iizuka, Haruna Takagi, Aika Kamei, Kazuei Hironaka, Hideharu Amano","doi":"10.1587/transinf.2023pap0009","DOIUrl":"https://doi.org/10.1587/transinf.2023pap0009","url":null,"abstract":"","PeriodicalId":48824,"journal":{"name":"IEICE Transactions on Information and Systems","volume":"82 s361","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138622583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1587/transinf.2023pap0011
Yuya Degawa, Toru Koizumi, Tomoki Nakamura, Ryota Shioya, J. Kadomoto, H. Irie, Shuichi Sakai
{"title":"A Principal Factor of Performance in Decoupled Front-End","authors":"Yuya Degawa, Toru Koizumi, Tomoki Nakamura, Ryota Shioya, J. Kadomoto, H. Irie, Shuichi Sakai","doi":"10.1587/transinf.2023pap0011","DOIUrl":"https://doi.org/10.1587/transinf.2023pap0011","url":null,"abstract":"","PeriodicalId":48824,"journal":{"name":"IEICE Transactions on Information and Systems","volume":"109 33","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138608499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1587/transinf.2022edp7229
Yuma Tsuchida, Kohei Kubo, Hisashi Koga
{"title":"Continuous Similarity Search for Dynamic Text Streams","authors":"Yuma Tsuchida, Kohei Kubo, Hisashi Koga","doi":"10.1587/transinf.2022edp7229","DOIUrl":"https://doi.org/10.1587/transinf.2022edp7229","url":null,"abstract":"","PeriodicalId":48824,"journal":{"name":"IEICE Transactions on Information and Systems","volume":" 8","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138615281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-01DOI: 10.1587/transinf.e96.d.772
Kenji Suzuki
Computer-aided detection (CADe) and diagnosis (CAD) has been a rapidly growing, active area of research in medical imaging. Machine leaning (ML) plays an essential role in CAD, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require "learning from examples." One of the most popular uses of ML is the classification of objects such as lesion candidates into certain classes (e.g., abnormal or normal, and lesions or non-lesions) based on input features (e.g., contrast and area) obtained from segmented lesion candidates. The task of ML is to determine "optimal" boundaries for separating classes in the multidimensional feature space which is formed by the input features. ML algorithms for classification include linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), multilayer perceptrons, and support vector machines (SVM). Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which uses pixel/voxel values in images directly, instead of features calculated from segmented lesions, as input information; thus, feature calculation or segmentation is not required. In this paper, ML techniques used in CAD schemes for detection and diagnosis of lung nodules in thoracic CT and for detection of polyps in CT colonography (CTC) are surveyed and reviewed.
{"title":"Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey.","authors":"Kenji Suzuki","doi":"10.1587/transinf.e96.d.772","DOIUrl":"https://doi.org/10.1587/transinf.e96.d.772","url":null,"abstract":"<p><p>Computer-aided detection (CADe) and diagnosis (CAD) has been a rapidly growing, active area of research in medical imaging. Machine leaning (ML) plays an essential role in CAD, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require \"learning from examples.\" One of the most popular uses of ML is the classification of objects such as lesion candidates into certain classes (e.g., abnormal or normal, and lesions or non-lesions) based on input features (e.g., contrast and area) obtained from segmented lesion candidates. The task of ML is to determine \"optimal\" boundaries for separating classes in the multidimensional feature space which is formed by the input features. ML algorithms for classification include linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), multilayer perceptrons, and support vector machines (SVM). Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which uses pixel/voxel values in images directly, instead of features calculated from segmented lesions, as input information; thus, feature calculation or segmentation is not required. In this paper, ML techniques used in CAD schemes for detection and diagnosis of lung nodules in thoracic CT and for detection of polyps in CT colonography (CTC) are surveyed and reviewed.</p>","PeriodicalId":48824,"journal":{"name":"IEICE Transactions on Information and Systems","volume":"E96-D 4","pages":"772-783"},"PeriodicalIF":0.7,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1587/transinf.e96.d.772","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40279760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-12-01DOI: 10.1093/ietisy/e90-d.12.2070
Y K Choi, J K Hahn
This paper addresses a new surface reconstruction scheme for approximating the isosurface from a set of tomographic cross sectional images. Differently from the novel Marching Cubes (MC) algorithm, our method does not extract the iso-density surface (isosurface) directly from the voxel data but calculates the iso-density point (isopoint) first. After building a coarse initial mesh approximating the ideal isosurface by the cell-boundary representation, it metamorphoses the mesh into the final isosurface by a relaxation scheme, called shrink-wrapping process. Compared with the MC algorithm, our method is robust and does not make any cracks on surface. Furthermore, since it is possible to utilize lots of additional isopoints during the surface reconstruction process by extending the adjacency definition, theoretically the resulting surface can be better in quality than the MC algorithm. According to experiments, it is proved to be very robust and efficient for isosurface reconstruction from cross sectional images.
{"title":"Shrink-wrapped isosurface from cross sectional images.","authors":"Y K Choi, J K Hahn","doi":"10.1093/ietisy/e90-d.12.2070","DOIUrl":"https://doi.org/10.1093/ietisy/e90-d.12.2070","url":null,"abstract":"<p><p>This paper addresses a new surface reconstruction scheme for approximating the isosurface from a set of tomographic cross sectional images. Differently from the novel Marching Cubes (MC) algorithm, our method does not extract the iso-density surface (isosurface) directly from the voxel data but calculates the iso-density point (isopoint) first. After building a coarse initial mesh approximating the ideal isosurface by the cell-boundary representation, it metamorphoses the mesh into the final isosurface by a relaxation scheme, called shrink-wrapping process. Compared with the MC algorithm, our method is robust and does not make any cracks on surface. Furthermore, since it is possible to utilize lots of additional isopoints during the surface reconstruction process by extending the adjacency definition, theoretically the resulting surface can be better in quality than the MC algorithm. According to experiments, it is proved to be very robust and efficient for isosurface reconstruction from cross sectional images.</p>","PeriodicalId":48824,"journal":{"name":"IEICE Transactions on Information and Systems","volume":"E90-D 12","pages":"2070-2076"},"PeriodicalIF":0.7,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2918906/pdf/nihms178075.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29182217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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