Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis
Sign Up to like & getrecommendations! Published in 2017 at "Computers in biology and medicine"
DOI: 10.1016/j.compbiomed.2017.04.006
Abstract: This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted… read more here.
Keywords: diagnosis; lung cancer; structured algorithms; cadx ... See more keywords
Measurement of deep structured experiences as a binary phenomenon
Sign Up to like & getrecommendations! Published in 2019 at "Annals of Leisure Research"
DOI: 10.1080/11745398.2018.1429285
Abstract: ABSTRACT We developed a procedure for measuring ‘deep structured experiences’ based on a conceptualization of such experiences being binary (i.e. present or absent at a given moment) rather than continuous. Deep structured experiences are heightened… read more here.
Keywords: structured experiences; binary phenomenon; experiences binary; measurement deep ... See more keywords
Retrieval of Physical Parameters With Deep Structured Kernel Regression
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3211554
Abstract: Retrieval of physical parameters is of paramount relevance for Earth monitoring. Statistical (machine) learning approaches have been successfully introduced in the community, because they can learn nonlinear functional relations from observational data with no strong… read more here.
Keywords: regression; physical parameters; structured kernel; deep structured ... See more keywords
Deep-Structured Machine Learning Model for the Recognition of Mixed-Defect Patterns in Semiconductor Fabrication Processes
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Semiconductor Manufacturing"
DOI: 10.1109/tsm.2018.2825482
Abstract: Semiconductor manufacturers aim to fabricate defect-free wafers in order to improve product quality, increase yields, and reduce costs. Typically, wafer defects form spatial patterns that provide useful information, helping to identify problems and faults during… read more here.
Keywords: machine learning; mixed defect; model; defect ... See more keywords