Matches in SemOpenAlex for { <https://semopenalex.org/work/W4367016760> ?p ?o ?g. }
- W4367016760 endingPage "334" @default.
- W4367016760 startingPage "323" @default.
- W4367016760 abstract "The ability to accurately and automatically segment surgical instruments is one of the important prerequisites for reasonable and stable operation of surgical robots. The utilization of deep learning in medical image segmentation has gained widespread recognition in recent years, leading to the proposition of multiple network models designed for the segmentation of diverse medical images, among which the most effective one is U-Net and its variants. Nevertheless, these existing networks also have various drawbacks, such as limited contextual representation capability, insufficient local feature processing, etc. In order to solve the above problems so that more accurate surgical instrument segmentation performance can be obtained, a transformer-based multi-scale attention network is proposed, referred to as TMA-Net, for surgical instrument segmentation from endoscopic images to serve robot-assisted surgery. To enable more accurate extraction of image features, a dual-branch encoder structure is proposed to obtain stronger contexts. Further, to address the problem that the simple skip connection is insufficient for local feature processing, an attention feature fusion (AFF) module and an additive attention and concatenation (AAC) module are proposed for effective feature learning to filter out the irrelevant information in the low-level features. Furthermore, a multi-scale context fusion (MCF) block is introduced to enhance the local feature maps and capture multi-scale contextual information. The efficacy of proposed TMA-Net is demonstrated through experimentation on publicly available surgical instrument segmentation datasets, including Endovis2017 and UW-Sinus-Surgery-C/L. The results show that proposed TMA-Net outperforms existing methods in terms of surgical instrument segmentation accuracy." @default.
- W4367016760 created "2023-04-27" @default.
- W4367016760 creator A5007493726 @default.
- W4367016760 creator A5033076846 @default.
- W4367016760 creator A5037783994 @default.
- W4367016760 creator A5048032713 @default.
- W4367016760 creator A5053636309 @default.
- W4367016760 creator A5083916527 @default.
- W4367016760 date "2023-05-01" @default.
- W4367016760 modified "2023-10-01" @default.
- W4367016760 title "TMA-Net: A Transformer-Based Multi-Scale Attention Network for Surgical Instrument Segmentation" @default.
- W4367016760 cites W1903029394 @default.
- W4367016760 cites W2109988760 @default.
- W4367016760 cites W2194775991 @default.
- W4367016760 cites W2412782625 @default.
- W4367016760 cites W2519007024 @default.
- W4367016760 cites W2962949934 @default.
- W4367016760 cites W2963881378 @default.
- W4367016760 cites W2979328438 @default.
- W4367016760 cites W2980225217 @default.
- W4367016760 cites W2996290406 @default.
- W4367016760 cites W2998675048 @default.
- W4367016760 cites W3008810159 @default.
- W4367016760 cites W3011743383 @default.
- W4367016760 cites W3089931213 @default.
- W4367016760 cites W3095139243 @default.
- W4367016760 cites W3096947210 @default.
- W4367016760 cites W3104724231 @default.
- W4367016760 cites W3110274389 @default.
- W4367016760 cites W3116069713 @default.
- W4367016760 cites W3129430687 @default.
- W4367016760 cites W3132466470 @default.
- W4367016760 cites W3142788400 @default.
- W4367016760 cites W3167876016 @default.
- W4367016760 cites W3180604690 @default.
- W4367016760 cites W4287891028 @default.
- W4367016760 cites W4292314562 @default.
- W4367016760 cites W4307296307 @default.
- W4367016760 cites W4309915773 @default.
- W4367016760 cites W4310381304 @default.
- W4367016760 cites W4311420498 @default.
- W4367016760 doi "https://doi.org/10.1109/tmrb.2023.3269856" @default.
- W4367016760 hasPublicationYear "2023" @default.
- W4367016760 type Work @default.
- W4367016760 citedByCount "1" @default.
- W4367016760 countsByYear W43670167602023 @default.
- W4367016760 crossrefType "journal-article" @default.
- W4367016760 hasAuthorship W4367016760A5007493726 @default.
- W4367016760 hasAuthorship W4367016760A5033076846 @default.
- W4367016760 hasAuthorship W4367016760A5037783994 @default.
- W4367016760 hasAuthorship W4367016760A5048032713 @default.
- W4367016760 hasAuthorship W4367016760A5053636309 @default.
- W4367016760 hasAuthorship W4367016760A5083916527 @default.
- W4367016760 hasConcept C111919701 @default.
- W4367016760 hasConcept C118505674 @default.
- W4367016760 hasConcept C119599485 @default.
- W4367016760 hasConcept C126838900 @default.
- W4367016760 hasConcept C127413603 @default.
- W4367016760 hasConcept C138885662 @default.
- W4367016760 hasConcept C151730666 @default.
- W4367016760 hasConcept C153180895 @default.
- W4367016760 hasConcept C154945302 @default.
- W4367016760 hasConcept C165801399 @default.
- W4367016760 hasConcept C2776401178 @default.
- W4367016760 hasConcept C2778181360 @default.
- W4367016760 hasConcept C2779343474 @default.
- W4367016760 hasConcept C2779370443 @default.
- W4367016760 hasConcept C31972630 @default.
- W4367016760 hasConcept C41008148 @default.
- W4367016760 hasConcept C41895202 @default.
- W4367016760 hasConcept C59404180 @default.
- W4367016760 hasConcept C66322947 @default.
- W4367016760 hasConcept C71924100 @default.
- W4367016760 hasConcept C78519656 @default.
- W4367016760 hasConcept C86803240 @default.
- W4367016760 hasConcept C89600930 @default.
- W4367016760 hasConcept C90509273 @default.
- W4367016760 hasConceptScore W4367016760C111919701 @default.
- W4367016760 hasConceptScore W4367016760C118505674 @default.
- W4367016760 hasConceptScore W4367016760C119599485 @default.
- W4367016760 hasConceptScore W4367016760C126838900 @default.
- W4367016760 hasConceptScore W4367016760C127413603 @default.
- W4367016760 hasConceptScore W4367016760C138885662 @default.
- W4367016760 hasConceptScore W4367016760C151730666 @default.
- W4367016760 hasConceptScore W4367016760C153180895 @default.
- W4367016760 hasConceptScore W4367016760C154945302 @default.
- W4367016760 hasConceptScore W4367016760C165801399 @default.
- W4367016760 hasConceptScore W4367016760C2776401178 @default.
- W4367016760 hasConceptScore W4367016760C2778181360 @default.
- W4367016760 hasConceptScore W4367016760C2779343474 @default.
- W4367016760 hasConceptScore W4367016760C2779370443 @default.
- W4367016760 hasConceptScore W4367016760C31972630 @default.
- W4367016760 hasConceptScore W4367016760C41008148 @default.
- W4367016760 hasConceptScore W4367016760C41895202 @default.
- W4367016760 hasConceptScore W4367016760C59404180 @default.
- W4367016760 hasConceptScore W4367016760C66322947 @default.
- W4367016760 hasConceptScore W4367016760C71924100 @default.
- W4367016760 hasConceptScore W4367016760C78519656 @default.