Matches in SemOpenAlex for { <https://semopenalex.org/work/W2963644511> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W2963644511 endingPage "115" @default.
- W2963644511 startingPage "105" @default.
- W2963644511 abstract "This article presents a convolutional neural network for the automatic segmentation of brain tumors in multimodal 3D MR images based on a U-net architecture. We evaluate the use of a densely connected convolutional network encoder (DenseNet) which was pretrained on the ImageNet data set. We detail two network architectures that can take into account multiple 3D images as inputs. This work aims to identify if a generic pretrained network can be used for very specific medical applications where the target data differ both in the number of spatial dimensions as well as in the number of inputs channels. Moreover in order to regularize this transfer learning task we only train the decoder part of the U-net architecture. We evaluate the effectiveness of the proposed approach on the BRATS 2018 segmentation challenge [1–5] where we obtained dice scores of 0.79, 0.90, 0.85 and 95% Hausdorff distance of 2.9 mm, 3.95 mm, and 6.48 mm for enhanced tumor core, whole tumor and tumor core respectively on the validation set. This scores degrades to 0.77, 0.88, 0.78 and 95% Hausdorff distance of 3.6 mm, 5.72 mm, and 5.83 mm on the testing set [1]." @default.
- W2963644511 created "2019-07-30" @default.
- W2963644511 creator A5022003832 @default.
- W2963644511 date "2019-01-01" @default.
- W2963644511 modified "2023-09-27" @default.
- W2963644511 title "A Pretrained DenseNet Encoder for Brain Tumor Segmentation" @default.
- W2963644511 cites W1641498739 @default.
- W2963644511 cites W1745334888 @default.
- W2963644511 cites W1903029394 @default.
- W2963644511 cites W2526009326 @default.
- W2963644511 cites W2604785265 @default.
- W2963644511 cites W2604790786 @default.
- W2963644511 cites W2751069891 @default.
- W2963644511 cites W2962914239 @default.
- W2963644511 cites W2963446712 @default.
- W2963644511 doi "https://doi.org/10.1007/978-3-030-11726-9_10" @default.
- W2963644511 hasPublicationYear "2019" @default.
- W2963644511 type Work @default.
- W2963644511 sameAs 2963644511 @default.
- W2963644511 citedByCount "7" @default.
- W2963644511 countsByYear W29636445112018 @default.
- W2963644511 countsByYear W29636445112020 @default.
- W2963644511 countsByYear W29636445112021 @default.
- W2963644511 countsByYear W29636445112022 @default.
- W2963644511 crossrefType "book-chapter" @default.
- W2963644511 hasAuthorship W2963644511A5022003832 @default.
- W2963644511 hasBestOaLocation W29636445112 @default.
- W2963644511 hasConcept C108583219 @default.
- W2963644511 hasConcept C111919701 @default.
- W2963644511 hasConcept C118505674 @default.
- W2963644511 hasConcept C124504099 @default.
- W2963644511 hasConcept C141898687 @default.
- W2963644511 hasConcept C153180895 @default.
- W2963644511 hasConcept C154945302 @default.
- W2963644511 hasConcept C177264268 @default.
- W2963644511 hasConcept C199360897 @default.
- W2963644511 hasConcept C22029948 @default.
- W2963644511 hasConcept C2524010 @default.
- W2963644511 hasConcept C31972630 @default.
- W2963644511 hasConcept C33923547 @default.
- W2963644511 hasConcept C41008148 @default.
- W2963644511 hasConcept C58489278 @default.
- W2963644511 hasConcept C81363708 @default.
- W2963644511 hasConcept C89600930 @default.
- W2963644511 hasConceptScore W2963644511C108583219 @default.
- W2963644511 hasConceptScore W2963644511C111919701 @default.
- W2963644511 hasConceptScore W2963644511C118505674 @default.
- W2963644511 hasConceptScore W2963644511C124504099 @default.
- W2963644511 hasConceptScore W2963644511C141898687 @default.
- W2963644511 hasConceptScore W2963644511C153180895 @default.
- W2963644511 hasConceptScore W2963644511C154945302 @default.
- W2963644511 hasConceptScore W2963644511C177264268 @default.
- W2963644511 hasConceptScore W2963644511C199360897 @default.
- W2963644511 hasConceptScore W2963644511C22029948 @default.
- W2963644511 hasConceptScore W2963644511C2524010 @default.
- W2963644511 hasConceptScore W2963644511C31972630 @default.
- W2963644511 hasConceptScore W2963644511C33923547 @default.
- W2963644511 hasConceptScore W2963644511C41008148 @default.
- W2963644511 hasConceptScore W2963644511C58489278 @default.
- W2963644511 hasConceptScore W2963644511C81363708 @default.
- W2963644511 hasConceptScore W2963644511C89600930 @default.
- W2963644511 hasLocation W29636445111 @default.
- W2963644511 hasLocation W29636445112 @default.
- W2963644511 hasOpenAccess W2963644511 @default.
- W2963644511 hasPrimaryLocation W29636445111 @default.
- W2963644511 hasRelatedWork W2731899572 @default.
- W2963644511 hasRelatedWork W2920218276 @default.
- W2963644511 hasRelatedWork W2960184797 @default.
- W2963644511 hasRelatedWork W3130357085 @default.
- W2963644511 hasRelatedWork W3133861977 @default.
- W2963644511 hasRelatedWork W4285827401 @default.
- W2963644511 hasRelatedWork W4297786172 @default.
- W2963644511 hasRelatedWork W4312417841 @default.
- W2963644511 hasRelatedWork W4319792695 @default.
- W2963644511 hasRelatedWork W4321369474 @default.
- W2963644511 isParatext "false" @default.
- W2963644511 isRetracted "false" @default.
- W2963644511 magId "2963644511" @default.
- W2963644511 workType "book-chapter" @default.