Matches in SemOpenAlex for { <https://semopenalex.org/work/W4307322182> ?p ?o ?g. }
Showing items 1 to 59 of
59
with 100 items per page.
- W4307322182 abstract "We propose Deep Kronecker Network (DKN), a novel framework designed for analyzing medical imaging data, such as MRI, fMRI, CT, etc. Medical imaging data is different from general images in at least two aspects: i) sample size is usually much more limited, ii) model interpretation is more of a concern compared to outcome prediction. Due to its unique nature, general methods, such as convolutional neural network (CNN), are difficult to be directly applied. As such, we propose DKN, that is able to i) adapt to low sample size limitation, ii) provide desired model interpretation, and iii) achieve the prediction power as CNN. The DKN is general in the sense that it not only works for both matrix and (high-order) tensor represented image data, but also could be applied to both discrete and continuous outcomes. The DKN is built on a Kronecker product structure and implicitly imposes a piecewise smooth property on coefficients. Moreover, the Kronecker structure can be written into a convolutional form, so DKN also resembles a CNN, particularly, a fully convolutional network (FCN). Furthermore, we prove that with an alternating minimization algorithm, the solutions of DKN are guaranteed to converge to the truth geometrically even if the objective function is highly nonconvex. Interestingly, the DKN is also highly connected to the tensor regression framework proposed by Zhou et al. (2010), where a CANDECOMP/PARAFAC (CP) low-rank structure is imposed on tensor coefficients. Finally, we conduct both classification and regression analyses using real MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to demonstrate the effectiveness of DKN." @default.
- W4307322182 created "2022-10-31" @default.
- W4307322182 creator A5062169644 @default.
- W4307322182 creator A5073429636 @default.
- W4307322182 date "2022-10-24" @default.
- W4307322182 modified "2023-09-27" @default.
- W4307322182 title "Deep Kronecker Network" @default.
- W4307322182 doi "https://doi.org/10.48550/arxiv.2210.13327" @default.
- W4307322182 hasPublicationYear "2022" @default.
- W4307322182 type Work @default.
- W4307322182 citedByCount "0" @default.
- W4307322182 crossrefType "posted-content" @default.
- W4307322182 hasAuthorship W4307322182A5062169644 @default.
- W4307322182 hasAuthorship W4307322182A5073429636 @default.
- W4307322182 hasBestOaLocation W43073221821 @default.
- W4307322182 hasConcept C11413529 @default.
- W4307322182 hasConcept C121332964 @default.
- W4307322182 hasConcept C154945302 @default.
- W4307322182 hasConcept C155281189 @default.
- W4307322182 hasConcept C199360897 @default.
- W4307322182 hasConcept C202444582 @default.
- W4307322182 hasConcept C2778770139 @default.
- W4307322182 hasConcept C33923547 @default.
- W4307322182 hasConcept C39482219 @default.
- W4307322182 hasConcept C41008148 @default.
- W4307322182 hasConcept C46030957 @default.
- W4307322182 hasConcept C527412718 @default.
- W4307322182 hasConcept C62520636 @default.
- W4307322182 hasConcept C81363708 @default.
- W4307322182 hasConceptScore W4307322182C11413529 @default.
- W4307322182 hasConceptScore W4307322182C121332964 @default.
- W4307322182 hasConceptScore W4307322182C154945302 @default.
- W4307322182 hasConceptScore W4307322182C155281189 @default.
- W4307322182 hasConceptScore W4307322182C199360897 @default.
- W4307322182 hasConceptScore W4307322182C202444582 @default.
- W4307322182 hasConceptScore W4307322182C2778770139 @default.
- W4307322182 hasConceptScore W4307322182C33923547 @default.
- W4307322182 hasConceptScore W4307322182C39482219 @default.
- W4307322182 hasConceptScore W4307322182C41008148 @default.
- W4307322182 hasConceptScore W4307322182C46030957 @default.
- W4307322182 hasConceptScore W4307322182C527412718 @default.
- W4307322182 hasConceptScore W4307322182C62520636 @default.
- W4307322182 hasConceptScore W4307322182C81363708 @default.
- W4307322182 hasLocation W43073221821 @default.
- W4307322182 hasOpenAccess W4307322182 @default.
- W4307322182 hasPrimaryLocation W43073221821 @default.
- W4307322182 hasRelatedWork W11543191 @default.
- W4307322182 hasRelatedWork W2108396447 @default.
- W4307322182 hasRelatedWork W2133497358 @default.
- W4307322182 hasRelatedWork W2139675554 @default.
- W4307322182 hasRelatedWork W2618655737 @default.
- W4307322182 hasRelatedWork W2772835661 @default.
- W4307322182 hasRelatedWork W2789582299 @default.
- W4307322182 hasRelatedWork W2964330822 @default.
- W4307322182 hasRelatedWork W4379258781 @default.
- W4307322182 hasRelatedWork W4298860571 @default.
- W4307322182 isParatext "false" @default.
- W4307322182 isRetracted "false" @default.
- W4307322182 workType "article" @default.