Matches in SemOpenAlex for { <https://semopenalex.org/work/W4367400186> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W4367400186 endingPage "102591" @default.
- W4367400186 startingPage "102591" @default.
- W4367400186 abstract "An accurate and well-defined survival prediction of High Grade Gliomas (HGGs) is indispensable because of its high incidence and aggressiveness. Therefore, this paper presents a unified framework for fully automatic overall survival classification and its interpretation.Initially, a glioma detection model is utilized to detect the tumorous images. A pre-processing module is designed for extracting 2D slices and creating a survival data array for the classification network. Then, the classification pipeline is integrated with two separate pathways: a modality-specific and a modality-concatenated pathway. The modality-specific pathway runs three separate CNNs for extracting rich predictive features from three sub-regions of HGGs (peritumoral edema, enhancing tumor and necrosis) by using three neuro-imaging modalities. In these pathways, the image vectors of the different modalities are also concatenated to the final fusion layer to overcome the loss of lower-level tumor features. Furthermore, to exploit the intra-modality correlations, a modality-concatenated pathway is also added to the classification pipeline. The experiments are conducted on BraTS 2018 and BraTS 2019 benchmarks, demonstrating that the proposed approach performs competitively in classifying HGG patients into three survival groups, namely, short, mid, and long survivors.The proposed approach achieves an overall classification accuracy, sensitivity, and specificity of about 0.998, 0.997, and 0.999, respectively, for the BraTS 2018 dataset, and for BraTS 2019, these values correspond to 1.000, 0.999, and 0.999.The results indicate that the proposed model achieves the highest values of the evaluation metrics for the overall survival classification of HGG." @default.
- W4367400186 created "2023-04-30" @default.
- W4367400186 creator A5034615664 @default.
- W4367400186 creator A5048143305 @default.
- W4367400186 creator A5057994698 @default.
- W4367400186 date "2023-06-01" @default.
- W4367400186 modified "2023-10-16" @default.
- W4367400186 title "An interpretable feature-learned model for overall survival classification of High-Grade Gliomas" @default.
- W4367400186 cites W1857532627 @default.
- W4367400186 cites W2058911076 @default.
- W4367400186 cites W2751538714 @default.
- W4367400186 cites W2761475227 @default.
- W4367400186 cites W2797541140 @default.
- W4367400186 cites W2903554604 @default.
- W4367400186 cites W2912884588 @default.
- W4367400186 cites W2963016155 @default.
- W4367400186 cites W3003478726 @default.
- W4367400186 cites W3004572460 @default.
- W4367400186 cites W3018852185 @default.
- W4367400186 cites W3036319923 @default.
- W4367400186 cites W3047143964 @default.
- W4367400186 cites W3105843050 @default.
- W4367400186 cites W3123241743 @default.
- W4367400186 cites W3135096391 @default.
- W4367400186 cites W3138961261 @default.
- W4367400186 cites W3140653050 @default.
- W4367400186 cites W3142243639 @default.
- W4367400186 cites W3146939089 @default.
- W4367400186 cites W3155555028 @default.
- W4367400186 cites W3192668314 @default.
- W4367400186 cites W4224271142 @default.
- W4367400186 cites W4281973755 @default.
- W4367400186 cites W4282928263 @default.
- W4367400186 doi "https://doi.org/10.1016/j.ejmp.2023.102591" @default.
- W4367400186 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37126962" @default.
- W4367400186 hasPublicationYear "2023" @default.
- W4367400186 type Work @default.
- W4367400186 citedByCount "0" @default.
- W4367400186 crossrefType "journal-article" @default.
- W4367400186 hasAuthorship W4367400186A5034615664 @default.
- W4367400186 hasAuthorship W4367400186A5048143305 @default.
- W4367400186 hasAuthorship W4367400186A5057994698 @default.
- W4367400186 hasBestOaLocation W43674001861 @default.
- W4367400186 hasConcept C138885662 @default.
- W4367400186 hasConcept C153180895 @default.
- W4367400186 hasConcept C154945302 @default.
- W4367400186 hasConcept C199360897 @default.
- W4367400186 hasConcept C2776401178 @default.
- W4367400186 hasConcept C2780226545 @default.
- W4367400186 hasConcept C41008148 @default.
- W4367400186 hasConcept C41895202 @default.
- W4367400186 hasConcept C43521106 @default.
- W4367400186 hasConceptScore W4367400186C138885662 @default.
- W4367400186 hasConceptScore W4367400186C153180895 @default.
- W4367400186 hasConceptScore W4367400186C154945302 @default.
- W4367400186 hasConceptScore W4367400186C199360897 @default.
- W4367400186 hasConceptScore W4367400186C2776401178 @default.
- W4367400186 hasConceptScore W4367400186C2780226545 @default.
- W4367400186 hasConceptScore W4367400186C41008148 @default.
- W4367400186 hasConceptScore W4367400186C41895202 @default.
- W4367400186 hasConceptScore W4367400186C43521106 @default.
- W4367400186 hasLocation W43674001861 @default.
- W4367400186 hasLocation W43674001862 @default.
- W4367400186 hasOpenAccess W4367400186 @default.
- W4367400186 hasPrimaryLocation W43674001861 @default.
- W4367400186 hasRelatedWork W2033914206 @default.
- W4367400186 hasRelatedWork W2146076056 @default.
- W4367400186 hasRelatedWork W2360883279 @default.
- W4367400186 hasRelatedWork W2382607599 @default.
- W4367400186 hasRelatedWork W2546942002 @default.
- W4367400186 hasRelatedWork W2560284304 @default.
- W4367400186 hasRelatedWork W2970216048 @default.
- W4367400186 hasRelatedWork W2992516105 @default.
- W4367400186 hasRelatedWork W3120899676 @default.
- W4367400186 hasRelatedWork W3210635025 @default.
- W4367400186 hasVolume "110" @default.
- W4367400186 isParatext "false" @default.
- W4367400186 isRetracted "false" @default.
- W4367400186 workType "article" @default.