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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2408.09261 |
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| _version_ | 1866911993252806656 |
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| author | Xiong, Zinan Chen, Shuijiao Zhang, Yizhe Cao, Yu Liu, Benyuan Liu, Xiaowei |
| author_facet | Xiong, Zinan Chen, Shuijiao Zhang, Yizhe Cao, Yu Liu, Benyuan Liu, Xiaowei |
| contents | Atrophic gastritis is a significant risk factor for developing gastric cancer. The incorporation of machine learning algorithms can efficiently elevate the possibility of accurately detecting atrophic gastritis. Nevertheless, when the trained model is applied in real-life circumstances, its output is often not consistently reliable. In this paper, we propose Adaptify, an adaptation scheme in which the model assimilates knowledge from its own classification decisions. Our proposed approach includes keeping the primary model constant, while simultaneously running and updating the auxiliary model. By integrating the knowledge gleaned by the auxiliary model into the primary model and merging their outputs, we have observed a notable improvement in output stability and consistency compared to relying solely on either the main model or the auxiliary model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09261 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptify: A Refined Adaptation Scheme for Frame Classification in Atrophic Gastritis Videos Xiong, Zinan Chen, Shuijiao Zhang, Yizhe Cao, Yu Liu, Benyuan Liu, Xiaowei Computer Vision and Pattern Recognition Atrophic gastritis is a significant risk factor for developing gastric cancer. The incorporation of machine learning algorithms can efficiently elevate the possibility of accurately detecting atrophic gastritis. Nevertheless, when the trained model is applied in real-life circumstances, its output is often not consistently reliable. In this paper, we propose Adaptify, an adaptation scheme in which the model assimilates knowledge from its own classification decisions. Our proposed approach includes keeping the primary model constant, while simultaneously running and updating the auxiliary model. By integrating the knowledge gleaned by the auxiliary model into the primary model and merging their outputs, we have observed a notable improvement in output stability and consistency compared to relying solely on either the main model or the auxiliary model. |
| title | Adaptify: A Refined Adaptation Scheme for Frame Classification in Atrophic Gastritis Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.09261 |