O-Net: A Novel Framework With Deep Fusion of CNN and Transformer for Simultaneous Segmentation and Classification Article Swipe
Tao Wang
,
Junlin Lan
,
Zixin Han
,
Ziwei Hu
,
Yuxiu Huang
,
Yanglin Deng
,
Hejun Zhang
,
Jianchao Wang
,
Musheng Chen
,
Haiyan Jiang
,
Ren-Guey Lee
,
Qinquan Gao
,
Ming Du
,
Tong Tong
,
Gang Chen
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2022.876065
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2022.876065
The application of deep learning in the medical field has continuously made huge breakthroughs in recent years. Based on convolutional neural network (CNN), the U-Net framework has become the benchmark of the medical image segmentation task. However, this framework cannot fully learn global information and remote semantic information. The transformer structure has been demonstrated to capture global information relatively better than the U-Net, but the ability to learn local information is not as good as CNN. Therefore, we propose a novel network referred to as the O-Net, which combines the advantages of CNN and transformer to fully use both the global and the local information for improving medical image segmentation and classification. In the encoder part of our proposed O-Net framework, we combine the CNN and the Swin Transformer to acquire both global and local contextual features. In the decoder part, the results of the Swin Transformer and the CNN blocks are fused to get the final results. We have evaluated the proposed network on the synapse multi-organ CT dataset and the ISIC 2017 challenge dataset for the segmentation task. The classification network is simultaneously trained by using the encoder weights of the segmentation network. The experimental results show that our proposed O-Net achieves superior segmentation performance than state-of-the-art approaches, and the segmentation results are beneficial for improving the accuracy of the classification task. The codes and models of this study are available at https://github.com/ortonwang/O-Net .
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnins.2022.876065
- https://www.frontiersin.org/articles/10.3389/fnins.2022.876065/pdf
- OA Status
- gold
- Cited By
- 45
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281631732
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Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4281631732Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fnins.2022.876065Digital Object Identifier
- Title
-
O-Net: A Novel Framework With Deep Fusion of CNN and Transformer for Simultaneous Segmentation and ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-02Full publication date if available
- Authors
-
Tao Wang, Junlin Lan, Zixin Han, Ziwei Hu, Yuxiu Huang, Yanglin Deng, Hejun Zhang, Jianchao Wang, Musheng Chen, Haiyan Jiang, Ren-Guey Lee, Qinquan Gao, Ming Du, Tong Tong, Gang ChenList of authors in order
- Landing page
-
https://doi.org/10.3389/fnins.2022.876065Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fnins.2022.876065/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/articles/10.3389/fnins.2022.876065/pdfDirect OA link when available
- Concepts
-
Computer science, Segmentation, Encoder, Convolutional neural network, Deep learning, Artificial intelligence, Transformer, Benchmark (surveying), Pattern recognition (psychology), Image segmentation, Machine learning, Physics, Operating system, Geography, Voltage, Quantum mechanics, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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45Total citation count in OpenAlex
- Citations by year (recent)
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2025: 14, 2024: 14, 2023: 13, 2022: 4Per-year citation counts (last 5 years)
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67Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.to | 54, 66, 83, 95, 129, 153 |
| abstract_inverted_index.we | 77, 121 |
| abstract_inverted_index.CNN | 92, 124, 149 |
| abstract_inverted_index.The | 0, 48, 180, 195, 224 |
| abstract_inverted_index.and | 44, 93, 101, 110, 125, 133, 147, 170, 210, 226 |
| abstract_inverted_index.are | 151, 214, 231 |
| abstract_inverted_index.but | 63 |
| abstract_inverted_index.for | 105, 176, 216 |
| abstract_inverted_index.get | 154 |
| abstract_inverted_index.has | 9, 26, 51 |
| abstract_inverted_index.not | 71 |
| abstract_inverted_index.our | 117, 200 |
| abstract_inverted_index.the | 6, 23, 28, 31, 61, 64, 85, 89, 99, 102, 113, 123, 126, 138, 141, 144, 148, 155, 161, 165, 171, 177, 188, 192, 211, 218, 221 |
| abstract_inverted_index.use | 97 |
| abstract_inverted_index.2017 | 173 |
| abstract_inverted_index.CNN. | 75 |
| abstract_inverted_index.ISIC | 172 |
| abstract_inverted_index.Swin | 127, 145 |
| abstract_inverted_index.been | 52 |
| abstract_inverted_index.both | 98, 131 |
| abstract_inverted_index.deep | 3 |
| abstract_inverted_index.good | 73 |
| abstract_inverted_index.have | 159 |
| abstract_inverted_index.huge | 12 |
| abstract_inverted_index.made | 11 |
| abstract_inverted_index.part | 115 |
| abstract_inverted_index.show | 198 |
| abstract_inverted_index.than | 60, 207 |
| abstract_inverted_index.that | 199 |
| abstract_inverted_index.this | 37, 229 |
| abstract_inverted_index.Based | 17 |
| abstract_inverted_index.O-Net | 119, 202 |
| abstract_inverted_index.U-Net | 24 |
| abstract_inverted_index.codes | 225 |
| abstract_inverted_index.field | 8 |
| abstract_inverted_index.final | 156 |
| abstract_inverted_index.fully | 40, 96 |
| abstract_inverted_index.fused | 152 |
| abstract_inverted_index.image | 33, 108 |
| abstract_inverted_index.learn | 41, 67 |
| abstract_inverted_index.local | 68, 103, 134 |
| abstract_inverted_index.novel | 80 |
| abstract_inverted_index.part, | 140 |
| abstract_inverted_index.study | 230 |
| abstract_inverted_index.task. | 35, 179, 223 |
| abstract_inverted_index.using | 187 |
| abstract_inverted_index.which | 87 |
| abstract_inverted_index.(CNN), | 22 |
| abstract_inverted_index.O-Net, | 86 |
| abstract_inverted_index.U-Net, | 62 |
| abstract_inverted_index.become | 27 |
| abstract_inverted_index.better | 59 |
| abstract_inverted_index.blocks | 150 |
| abstract_inverted_index.cannot | 39 |
| abstract_inverted_index.global | 42, 56, 100, 132 |
| abstract_inverted_index.models | 227 |
| abstract_inverted_index.neural | 20 |
| abstract_inverted_index.recent | 15 |
| abstract_inverted_index.remote | 45 |
| abstract_inverted_index.years. | 16 |
| abstract_inverted_index.ability | 65 |
| abstract_inverted_index.acquire | 130 |
| abstract_inverted_index.capture | 55 |
| abstract_inverted_index.combine | 122 |
| abstract_inverted_index.dataset | 169, 175 |
| abstract_inverted_index.decoder | 139 |
| abstract_inverted_index.encoder | 114, 189 |
| abstract_inverted_index.medical | 7, 32, 107 |
| abstract_inverted_index.network | 21, 81, 163, 182 |
| abstract_inverted_index.propose | 78 |
| abstract_inverted_index.results | 142, 197, 213 |
| abstract_inverted_index.synapse | 166 |
| abstract_inverted_index.trained | 185 |
| abstract_inverted_index.weights | 190 |
| abstract_inverted_index.However, | 36 |
| abstract_inverted_index.accuracy | 219 |
| abstract_inverted_index.achieves | 203 |
| abstract_inverted_index.combines | 88 |
| abstract_inverted_index.learning | 4 |
| abstract_inverted_index.network. | 194 |
| abstract_inverted_index.proposed | 118, 162, 201 |
| abstract_inverted_index.referred | 82 |
| abstract_inverted_index.results. | 157 |
| abstract_inverted_index.semantic | 46 |
| abstract_inverted_index.superior | 204 |
| abstract_inverted_index.available | 232 |
| abstract_inverted_index.benchmark | 29 |
| abstract_inverted_index.challenge | 174 |
| abstract_inverted_index.evaluated | 160 |
| abstract_inverted_index.features. | 136 |
| abstract_inverted_index.framework | 25, 38 |
| abstract_inverted_index.improving | 106, 217 |
| abstract_inverted_index.structure | 50 |
| abstract_inverted_index.Therefore, | 76 |
| abstract_inverted_index.advantages | 90 |
| abstract_inverted_index.beneficial | 215 |
| abstract_inverted_index.contextual | 135 |
| abstract_inverted_index.framework, | 120 |
| abstract_inverted_index.relatively | 58 |
| abstract_inverted_index.Transformer | 128, 146 |
| abstract_inverted_index.application | 1 |
| abstract_inverted_index.approaches, | 209 |
| abstract_inverted_index.information | 43, 57, 69, 104 |
| abstract_inverted_index.multi-organ | 167 |
| abstract_inverted_index.performance | 206 |
| abstract_inverted_index.transformer | 49, 94 |
| abstract_inverted_index.continuously | 10 |
| abstract_inverted_index.demonstrated | 53 |
| abstract_inverted_index.experimental | 196 |
| abstract_inverted_index.information. | 47 |
| abstract_inverted_index.segmentation | 34, 109, 178, 193, 205, 212 |
| abstract_inverted_index.breakthroughs | 13 |
| abstract_inverted_index.convolutional | 19 |
| abstract_inverted_index.classification | 181, 222 |
| abstract_inverted_index.simultaneously | 184 |
| abstract_inverted_index.classification. | 111 |
| abstract_inverted_index.state-of-the-art | 208 |
| abstract_inverted_index.https://github.com/ortonwang/O-Net | 234 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5100389319, https://openalex.org/A5100745675 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 15 |
| corresponding_institution_ids | https://openalex.org/I129708740, https://openalex.org/I4210148548, https://openalex.org/I80947539 |
| citation_normalized_percentile.value | 0.97724576 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |