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- Publisher Website: 10.1007/978-3-030-59725-2_60
- Scopus: eid_2-s2.0-85092762216
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Conference Paper: Towards Robust Bone Age Assessment: Rethinking Label Noise and Ambiguity
Title | Towards Robust Bone Age Assessment: Rethinking Label Noise and Ambiguity |
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Authors | |
Keywords | Bone age assessment Noisy label Graph convolutional network |
Issue Date | 2020 |
Publisher | Springer. |
Citation | The 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Virtual Conference, Lima, Peru, 4-8 October 2020. In MICCAI 2020 Proceedings, Part 6, p. 621-630 How to Cite? |
Abstract | The effects of label noise and ambiguity are widespread, especially for subjective tasks such as bone age assessment (BAA). However, most existing BAA algorithms ignore these issues. We propose a robust framework for BAA supporting Tanner & Whitehouse 3 (TW3) method, which is clinically more objective and reproducible than Greulich & Pyle (GP) method, but has received less attention from the research community. Since the publicly available RSNA BAA dataset was annotated using GP method, we contribute additional TW3 annotations. We formulate TW3 BAA as an ordinal regression problem, and address both label noise and ambiguity with a two stage deep learning framework. The first stage focuses on correcting erroneous labels with ambiguity tolerated, while the latter stage introduces a module called Residual Context Graph (RCG) to conquer label ambiguity. Inspired by the way human experts handle ambiguity, we combine fine-grained local features with a graph based context. Experiments show the proposed framework outperforms previously reported TW3-based BAA systems by large margins. TW3 annotations of bone maturity levels for a portion of the RSNA BAA dataset will be made publicly available. |
Persistent Identifier | http://hdl.handle.net/10722/301297 |
ISBN | |
Series/Report no. | Lecture Notes in Computer Science (LNCS) ; v. 12266 |
DC Field | Value | Language |
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dc.contributor.author | Gong, P | - |
dc.contributor.author | Yin, Z | - |
dc.contributor.author | Wang, Y | - |
dc.contributor.author | Yu, Y | - |
dc.date.accessioned | 2021-07-27T08:09:02Z | - |
dc.date.available | 2021-07-27T08:09:02Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | The 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Virtual Conference, Lima, Peru, 4-8 October 2020. In MICCAI 2020 Proceedings, Part 6, p. 621-630 | - |
dc.identifier.isbn | 9783030597245 | - |
dc.identifier.uri | http://hdl.handle.net/10722/301297 | - |
dc.description.abstract | The effects of label noise and ambiguity are widespread, especially for subjective tasks such as bone age assessment (BAA). However, most existing BAA algorithms ignore these issues. We propose a robust framework for BAA supporting Tanner & Whitehouse 3 (TW3) method, which is clinically more objective and reproducible than Greulich & Pyle (GP) method, but has received less attention from the research community. Since the publicly available RSNA BAA dataset was annotated using GP method, we contribute additional TW3 annotations. We formulate TW3 BAA as an ordinal regression problem, and address both label noise and ambiguity with a two stage deep learning framework. The first stage focuses on correcting erroneous labels with ambiguity tolerated, while the latter stage introduces a module called Residual Context Graph (RCG) to conquer label ambiguity. Inspired by the way human experts handle ambiguity, we combine fine-grained local features with a graph based context. Experiments show the proposed framework outperforms previously reported TW3-based BAA systems by large margins. TW3 annotations of bone maturity levels for a portion of the RSNA BAA dataset will be made publicly available. | - |
dc.language | eng | - |
dc.publisher | Springer. | - |
dc.relation.ispartof | International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science (LNCS) ; v. 12266 | - |
dc.subject | Bone age assessment | - |
dc.subject | Noisy label | - |
dc.subject | Graph convolutional network | - |
dc.title | Towards Robust Bone Age Assessment: Rethinking Label Noise and Ambiguity | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Yu, Y: yzyu@cs.hku.hk | - |
dc.identifier.authority | Yu, Y=rp01415 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-030-59725-2_60 | - |
dc.identifier.scopus | eid_2-s2.0-85092762216 | - |
dc.identifier.hkuros | 323539 | - |
dc.identifier.volume | Part 6 | - |
dc.identifier.spage | 621 | - |
dc.identifier.epage | 630 | - |
dc.publisher.place | Cham | - |