Convolutional neural network with packing mechanism and color transfer post processing for extended depth of field cervical cytology images

• Item type: SIG Technical Reports(1)
• 公開日: 2024-11-27
• タイトル: Convolutional neural network with packing mechanism and color transfer post processing for extended depth of field cervical cytology images
• タイトル / 言語: en
• タイトル: Convolutional neural network with packing mechanism and color transfer post processing for extended depth of field cervical cytology images
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Graduate School of Natural Science and Technology, Kanazawa University／Human Resocia
• 著者所属: Graduate School of Natural Science and Technology, Kanazawa University
• 著者所属: Graduate School of Natural Science and Technology, Kanazawa University
• 著者所属: Graduate School of Natural Science and Technology, Kanazawa University
• 著者所属: Institute of Transdisciplinary Sciences for Innovation, Kanazawa University
• 著者所属(英): en
• 著者所属(英): Graduate School of Natural Science and Technology, Kanazawa University / Human Resocia
• 著者所属(英): en
• 著者所属(英): Graduate School of Natural Science and Technology, Kanazawa University
• 著者所属(英): en
• 著者所属(英): Graduate School of Natural Science and Technology, Kanazawa University
• 著者所属(英): en
• 著者所属(英): Graduate School of Natural Science and Technology, Kanazawa University
• 著者所属(英): en
• 著者所属(英): Institute of Transdisciplinary Sciences for Innovation, Kanazawa University
• 著者名: Thanawat, Tangpornpisit; Lilies, Handayani; Denis, Chegodaev; Rik, Gijs Gerrit Raes; Kenji, Satou
• 著者名(英): Thanawat, Tangpornpisit; Lilies, Handayani; Denis, Chegodaev; Rik, Gijs Gerrit Raes; Kenji, Satou
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: The application of deep learning to the microscopic Extended Depth of Field (EDoF) images generation is on the rise as an alternative to the traditional time-consuming method. In this research, the packing mechanism from the self-driving car field is incorporated into the cytology images problem. Additionally, color transfer algorithm was selected as a post-processing method. We proposed novel models for both grayscale and RGB images. The evaluation of the model is then compared with the state-of-the-art and significant improvement was discovered with the metrics of Mean Square Error (MSE), Root Mean Square Error (RMSE), Peak Signal-to-Noise-Ratio (PSNR), and Structural Similarity Index (SSIM).
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: The application of deep learning to the microscopic Extended Depth of Field (EDoF) images generation is on the rise as an alternative to the traditional time-consuming method. In this research, the packing mechanism from the self-driving car field is incorporated into the cytology images problem. Additionally, color transfer algorithm was selected as a post-processing method. We proposed novel models for both grayscale and RGB images. The evaluation of the model is then compared with the state-of-the-art and significant improvement was discovered with the metrics of Mean Square Error (MSE), Root Mean Square Error (RMSE), Peak Signal-to-Noise-Ratio (PSNR), and Structural Similarity Index (SSIM).
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12055912
• 書誌情報: 研究報告バイオ情報学（BIO）; 巻 2024-BIO-80, 号 12, p. 1-6, 発行日 2024-11-27
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8590
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会