Matches in SemOpenAlex for { <https://semopenalex.org/work/W3129553451> ?p ?o ?g. }
Showing items 1 to 70 of
70
with 100 items per page.
- W3129553451 abstract "Surgical tool segmentation in endoscopic images is an important problem: it is a crucial step towards full instrument pose estimation and it is used for integration of pre- and intra-operative images into the endoscopic view. While many recent approaches based on convolutional neural networks have shown great results, a key barrier to progress lies in the acquisition of a large number of manually-annotated images which is necessary for an algorithm to generalize and work well in diverse surgical scenarios. Unlike the surgical image data itself, annotations are difficult to acquire and may be of variable quality. On the other hand, synthetic annotations can be automatically generated by using forward kinematic model of the robot and CAD models of tools by projecting them onto an image plane. Unfortunately, this model is very inaccurate and cannot be used for supervised learning of image segmentation models. Since generated annotations will not directly correspond to endoscopic images due to errors, we formulate the problem as an unpaired image-to-image translation where the goal is to learn the mapping between an input endoscopic image and a corresponding annotation using an adversarial model. Our approach allows to train image segmentation models without the need to acquire expensive annotations and can potentially exploit large unlabeled endoscopic image collection outside the annotated distributions of image/annotation data. We test our proposed method on Endovis 2017 challenge dataset and show that it is competitive with supervised segmentation methods." @default.
- W3129553451 created "2021-03-01" @default.
- W3129553451 creator A5028637670 @default.
- W3129553451 creator A5046896448 @default.
- W3129553451 creator A5048353325 @default.
- W3129553451 date "2020-10-24" @default.
- W3129553451 modified "2023-09-23" @default.
- W3129553451 title "Towards Unsupervised Learning for Instrument Segmentation in Robotic Surgery with Cycle-Consistent Adversarial Networks" @default.
- W3129553451 cites W1881731092 @default.
- W3129553451 cites W1903029394 @default.
- W3129553451 cites W2031489346 @default.
- W3129553451 cites W2091695913 @default.
- W3129553451 cites W2109815552 @default.
- W3129553451 cites W2138495364 @default.
- W3129553451 cites W2179331991 @default.
- W3129553451 cites W2194775991 @default.
- W3129553451 cites W2340897893 @default.
- W3129553451 cites W2604690505 @default.
- W3129553451 cites W2962793481 @default.
- W3129553451 cites W2962804068 @default.
- W3129553451 cites W2962936819 @default.
- W3129553451 cites W2964166015 @default.
- W3129553451 cites W2979609563 @default.
- W3129553451 cites W4211209029 @default.
- W3129553451 doi "https://doi.org/10.1109/iros45743.2020.9340816" @default.
- W3129553451 hasPublicationYear "2020" @default.
- W3129553451 type Work @default.
- W3129553451 sameAs 3129553451 @default.
- W3129553451 citedByCount "7" @default.
- W3129553451 countsByYear W31295534512021 @default.
- W3129553451 countsByYear W31295534512022 @default.
- W3129553451 crossrefType "proceedings-article" @default.
- W3129553451 hasAuthorship W3129553451A5028637670 @default.
- W3129553451 hasAuthorship W3129553451A5046896448 @default.
- W3129553451 hasAuthorship W3129553451A5048353325 @default.
- W3129553451 hasBestOaLocation W31295534512 @default.
- W3129553451 hasConcept C119857082 @default.
- W3129553451 hasConcept C154945302 @default.
- W3129553451 hasConcept C31972630 @default.
- W3129553451 hasConcept C37736160 @default.
- W3129553451 hasConcept C41008148 @default.
- W3129553451 hasConcept C8038995 @default.
- W3129553451 hasConcept C89600930 @default.
- W3129553451 hasConcept C90509273 @default.
- W3129553451 hasConceptScore W3129553451C119857082 @default.
- W3129553451 hasConceptScore W3129553451C154945302 @default.
- W3129553451 hasConceptScore W3129553451C31972630 @default.
- W3129553451 hasConceptScore W3129553451C37736160 @default.
- W3129553451 hasConceptScore W3129553451C41008148 @default.
- W3129553451 hasConceptScore W3129553451C8038995 @default.
- W3129553451 hasConceptScore W3129553451C89600930 @default.
- W3129553451 hasConceptScore W3129553451C90509273 @default.
- W3129553451 hasLocation W31295534511 @default.
- W3129553451 hasLocation W31295534512 @default.
- W3129553451 hasOpenAccess W3129553451 @default.
- W3129553451 hasPrimaryLocation W31295534511 @default.
- W3129553451 hasRelatedWork W1669643531 @default.
- W3129553451 hasRelatedWork W2005437358 @default.
- W3129553451 hasRelatedWork W2008656436 @default.
- W3129553451 hasRelatedWork W2023558673 @default.
- W3129553451 hasRelatedWork W2039154422 @default.
- W3129553451 hasRelatedWork W2122581818 @default.
- W3129553451 hasRelatedWork W2134924024 @default.
- W3129553451 hasRelatedWork W2517104666 @default.
- W3129553451 hasRelatedWork W2895616727 @default.
- W3129553451 hasRelatedWork W2182382398 @default.
- W3129553451 isParatext "false" @default.
- W3129553451 isRetracted "false" @default.
- W3129553451 magId "3129553451" @default.
- W3129553451 workType "article" @default.