FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285403713> ?p ?o ?g. }

• W4285403713 endingPage "102363" @default.
• W4285403713 startingPage "102363" @default.
• W4285403713 abstract "Deep learning based computer-aided diagnosis technology demonstrates an encouraging performance in aspect of polyp lesion detection on reducing the miss rate of polyps during colonoscopies. However, to date, few studies have been conducted for tracking polyps that have been detected in colonoscopy videos, which is an essential and intuitive issue in clinical intelligent video analysis task (e.g. lesion counting, lesion retrieval, report generation). In the paradigm of conventional tracking-by-detection system, detection task for lesion localization is separated from the tracking task for cropped lesions re-identification. In the multi object tracking problem, each target is supposed to be tracked by invoking a tracker after the detector, which introduces multiple inferences and leads to external resource and time consumption. To tackle these problems, we proposed a plug-in module named instance tracking head (ITH) for synchronous polyp detection and tracking, which can be simply inserted into object detection frameworks. It embeds a feature-based polyp tracking procedure into the detector frameworks to achieve multi-task model training. ITH and detection head share the model backbone for low level feature extraction, and then low level feature flows into the separate branches for task-driven model training. For feature maps from the same receptive field, the region of interest head assigns these features to the detection head and the ITH, respectively, and outputs the object category, bounding box coordinates, and instance feature embedding simultaneously for each specific polyp target. We also proposed a method based on similarity metric learning. The method makes full use of the prior boxes in the object detector to provide richer and denser instance training pairs, to improve the performance of the model evaluation on the tracking task. Compared with advanced tracking-by-detection paradigm methods, detectors with proposed ITH can obtain comparative tracking performance but approximate 30% faster speed. Optimized model based on Scaled-YOLOv4 detector with ITH illustrates good trade-off between detection (mAP 91.70%) and tracking (MOTA 92.50% and Rank-1 Acc 88.31%) task at the frame rate of 66 FPS. The proposed structure demonstrates the potential to aid clinicians in real-time detection with online tracking or offline retargeting of polyp instances during colonoscopies." @default.
• W4285403713 created "2022-07-14" @default.
• W4285403713 creator A5003995458 @default.
• W4285403713 creator A5019397977 @default.
• W4285403713 creator A5048274839 @default.
• W4285403713 creator A5048724398 @default.
• W4285403713 creator A5052308910 @default.
• W4285403713 creator A5056038703 @default.
• W4285403713 creator A5056801542 @default.
• W4285403713 creator A5059296415 @default.
• W4285403713 creator A5062375801 @default.
• W4285403713 creator A5070231479 @default.
• W4285403713 date "2022-09-01" @default.
• W4285403713 modified "2023-10-14" @default.
• W4285403713 title "An end-to-end tracking method for polyp detectors in colonoscopy videos" @default.
• W4285403713 cites W1963756842 @default.
• W4285403713 cites W1998557720 @default.
• W4285403713 cites W2008359794 @default.
• W4285403713 cites W2021088830 @default.
• W4285403713 cites W2067191022 @default.
• W4285403713 cites W2131101814 @default.
• W4285403713 cites W2140753590 @default.
• W4285403713 cites W2151103935 @default.
• W4285403713 cites W2163696938 @default.
• W4285403713 cites W2167546181 @default.
• W4285403713 cites W2336239503 @default.
• W4285403713 cites W2410282112 @default.
• W4285403713 cites W2560014990 @default.
• W4285403713 cites W2560770519 @default.
• W4285403713 cites W2584038571 @default.
• W4285403713 cites W2592929672 @default.
• W4285403713 cites W2752119587 @default.
• W4285403713 cites W2806175674 @default.
• W4285403713 cites W2809596283 @default.
• W4285403713 cites W2884985635 @default.
• W4285403713 cites W2888447104 @default.
• W4285403713 cites W2905189062 @default.
• W4285403713 cites W2911188335 @default.
• W4285403713 cites W2919115771 @default.
• W4285403713 cites W2943929690 @default.
• W4285403713 cites W3134856831 @default.
• W4285403713 cites W3138768734 @default.
• W4285403713 cites W4231576061 @default.
• W4285403713 cites W4241413943 @default.
• W4285403713 doi "https://doi.org/10.1016/j.artmed.2022.102363" @default.
• W4285403713 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36100343" @default.
• W4285403713 hasPublicationYear "2022" @default.
• W4285403713 type Work @default.
• W4285403713 citedByCount "2" @default.
• W4285403713 countsByYear W42854037132023 @default.
• W4285403713 crossrefType "journal-article" @default.
• W4285403713 hasAuthorship W4285403713A5003995458 @default.
• W4285403713 hasAuthorship W4285403713A5019397977 @default.
• W4285403713 hasAuthorship W4285403713A5048274839 @default.
• W4285403713 hasAuthorship W4285403713A5048724398 @default.
• W4285403713 hasAuthorship W4285403713A5052308910 @default.
• W4285403713 hasAuthorship W4285403713A5056038703 @default.
• W4285403713 hasAuthorship W4285403713A5056801542 @default.
• W4285403713 hasAuthorship W4285403713A5059296415 @default.
• W4285403713 hasAuthorship W4285403713A5062375801 @default.
• W4285403713 hasAuthorship W4285403713A5070231479 @default.
• W4285403713 hasConcept C108583219 @default.
• W4285403713 hasConcept C115961682 @default.
• W4285403713 hasConcept C138885662 @default.
• W4285403713 hasConcept C147037132 @default.
• W4285403713 hasConcept C153180895 @default.
• W4285403713 hasConcept C154945302 @default.
• W4285403713 hasConcept C15744967 @default.
• W4285403713 hasConcept C162324750 @default.
• W4285403713 hasConcept C187736073 @default.
• W4285403713 hasConcept C19417346 @default.
• W4285403713 hasConcept C202474056 @default.
• W4285403713 hasConcept C2775936607 @default.
• W4285403713 hasConcept C2776151529 @default.
• W4285403713 hasConcept C2776401178 @default.
• W4285403713 hasConcept C2780451532 @default.
• W4285403713 hasConcept C2781238097 @default.
• W4285403713 hasConcept C31972630 @default.
• W4285403713 hasConcept C41008148 @default.
• W4285403713 hasConcept C41895202 @default.
• W4285403713 hasConcept C52622490 @default.
• W4285403713 hasConceptScore W4285403713C108583219 @default.
• W4285403713 hasConceptScore W4285403713C115961682 @default.
• W4285403713 hasConceptScore W4285403713C138885662 @default.
• W4285403713 hasConceptScore W4285403713C147037132 @default.
• W4285403713 hasConceptScore W4285403713C153180895 @default.
• W4285403713 hasConceptScore W4285403713C154945302 @default.
• W4285403713 hasConceptScore W4285403713C15744967 @default.
• W4285403713 hasConceptScore W4285403713C162324750 @default.
• W4285403713 hasConceptScore W4285403713C187736073 @default.
• W4285403713 hasConceptScore W4285403713C19417346 @default.
• W4285403713 hasConceptScore W4285403713C202474056 @default.
• W4285403713 hasConceptScore W4285403713C2775936607 @default.
• W4285403713 hasConceptScore W4285403713C2776151529 @default.
• W4285403713 hasConceptScore W4285403713C2776401178 @default.
• W4285403713 hasConceptScore W4285403713C2780451532 @default.
• W4285403713 hasConceptScore W4285403713C2781238097 @default.
• W4285403713 hasConceptScore W4285403713C31972630 @default.

• next