Matches in SemOpenAlex for { <https://semopenalex.org/work/W3094363074> ?p ?o ?g. }
Showing items 1 to 70 of
70
with 100 items per page.
- W3094363074 abstract "Now-a-days artificial intelligence has huge impact on medical field and outcompeted the conventional methods that are being used. In clinical fluoroscopy procedures most of the annotated data is not archived. Hence there is lack of meaningful labelled data in radiography. To generate this data from three dimensional annotated computed tomography volume using ray casting is known as Digitally Reconstructed Radiograph (DRR). This radiograph should be very similar to the real one by adding functionality such as attenuation and different parameters. This parameters are similar to the actual system pipeline of generating x-ray. The parameters include kV, mA, rotation and translation. The pipeline also consists of scatter estimation, dynamic range management, noise generation, etc. Multiple DRRs can be generated using the proposed pipeline with given transformations and learning targets. This work focuses on improving quality and speed of DRR generation. The notable amount of diverse data can be retrieved by applying data augmentation on the given small data. Thus this DRRs can be used for training, testing and validating different task based specific AI/ML models. These DRRs can be used for various tasks in x-ray imaging as classification, anatomical landmark detection, vessel segmentation, etc. The proposed solution in this work has taken 1.2 seconds on average to generate DRR of image size 1000*1000 pixel. This implies the speed of writing as 1.5 µs per pixel." @default.
- W3094363074 created "2020-10-29" @default.
- W3094363074 creator A5007137110 @default.
- W3094363074 creator A5018089267 @default.
- W3094363074 creator A5031307793 @default.
- W3094363074 date "2020-07-01" @default.
- W3094363074 modified "2023-10-16" @default.
- W3094363074 title "Digitally Reconstructed Radiograph Generation for Enabling AI/ML in Medical Imaging" @default.
- W3094363074 cites W2133255533 @default.
- W3094363074 cites W2151593622 @default.
- W3094363074 cites W2182785488 @default.
- W3094363074 cites W2253429366 @default.
- W3094363074 cites W2334763311 @default.
- W3094363074 doi "https://doi.org/10.1109/icccnt49239.2020.9225465" @default.
- W3094363074 hasPublicationYear "2020" @default.
- W3094363074 type Work @default.
- W3094363074 sameAs 3094363074 @default.
- W3094363074 citedByCount "1" @default.
- W3094363074 countsByYear W30943630742022 @default.
- W3094363074 crossrefType "proceedings-article" @default.
- W3094363074 hasAuthorship W3094363074A5007137110 @default.
- W3094363074 hasAuthorship W3094363074A5018089267 @default.
- W3094363074 hasAuthorship W3094363074A5031307793 @default.
- W3094363074 hasConcept C115961682 @default.
- W3094363074 hasConcept C137800194 @default.
- W3094363074 hasConcept C141071460 @default.
- W3094363074 hasConcept C154945302 @default.
- W3094363074 hasConcept C160633673 @default.
- W3094363074 hasConcept C199360897 @default.
- W3094363074 hasConcept C2776805002 @default.
- W3094363074 hasConcept C31601959 @default.
- W3094363074 hasConcept C31972630 @default.
- W3094363074 hasConcept C41008148 @default.
- W3094363074 hasConcept C43521106 @default.
- W3094363074 hasConcept C55020928 @default.
- W3094363074 hasConcept C71924100 @default.
- W3094363074 hasConcept C89600930 @default.
- W3094363074 hasConcept C99498987 @default.
- W3094363074 hasConceptScore W3094363074C115961682 @default.
- W3094363074 hasConceptScore W3094363074C137800194 @default.
- W3094363074 hasConceptScore W3094363074C141071460 @default.
- W3094363074 hasConceptScore W3094363074C154945302 @default.
- W3094363074 hasConceptScore W3094363074C160633673 @default.
- W3094363074 hasConceptScore W3094363074C199360897 @default.
- W3094363074 hasConceptScore W3094363074C2776805002 @default.
- W3094363074 hasConceptScore W3094363074C31601959 @default.
- W3094363074 hasConceptScore W3094363074C31972630 @default.
- W3094363074 hasConceptScore W3094363074C41008148 @default.
- W3094363074 hasConceptScore W3094363074C43521106 @default.
- W3094363074 hasConceptScore W3094363074C55020928 @default.
- W3094363074 hasConceptScore W3094363074C71924100 @default.
- W3094363074 hasConceptScore W3094363074C89600930 @default.
- W3094363074 hasConceptScore W3094363074C99498987 @default.
- W3094363074 hasLocation W30943630741 @default.
- W3094363074 hasOpenAccess W3094363074 @default.
- W3094363074 hasPrimaryLocation W30943630741 @default.
- W3094363074 hasRelatedWork W121273120 @default.
- W3094363074 hasRelatedWork W1669643531 @default.
- W3094363074 hasRelatedWork W2008656436 @default.
- W3094363074 hasRelatedWork W2023558673 @default.
- W3094363074 hasRelatedWork W2039154422 @default.
- W3094363074 hasRelatedWork W2134924024 @default.
- W3094363074 hasRelatedWork W2337415362 @default.
- W3094363074 hasRelatedWork W2507402573 @default.
- W3094363074 hasRelatedWork W2517104666 @default.
- W3094363074 hasRelatedWork W2895616727 @default.
- W3094363074 isParatext "false" @default.
- W3094363074 isRetracted "false" @default.
- W3094363074 magId "3094363074" @default.
- W3094363074 workType "article" @default.