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Attention-effective multiple instance learning on weakly stem cell colony segmentation
http://hdl.handle.net/10069/00042053
http://hdl.handle.net/10069/0004205324d9f625-6b7e-4aca-85a6-103aef4920fe
名前 / ファイル | ライセンス | アクション |
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ISA17_200187.pdf (12.1 MB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2023-02-14 | |||||
タイトル | ||||||
タイトル | Attention-effective multiple instance learning on weakly stem cell colony segmentation | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Multiple instance | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Weakly supervised segmentation | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Colony | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Annotation | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Inference | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者 |
Yudistira, Novanto
× Yudistira, Novanto× Kavitha, Muthu Subash× Rajan, Jeny× Kurita, Takio |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | The detection of induced pluripotent stem cell (iPSC) colonies often needs the precise extraction of the colony features. However, existing computerized systems relied on segmentation of contours by preprocessing for classifying the colony conditions were task-extensive. To maximize the efficiency in categorizing colony conditions, we propose a multiple instance learning (MIL) in weakly supervised settings. It is designed in a single model to produce weak segmentation and classification of colonies without using finely labeled samples. As a single model, we employ a U-net-like convolution neural network (CNN) to train on binary image-level labels for MIL colonies classification. Furthermore, to specify the object of interest we used a simple post-processing method. The proposed approach is compared over conventional methods using five-fold cross-validation and receiver operating characteristic (ROC) curve. The maximum accuracy of the MIL-net is 95%, which is 15% higher than the conventional methods. Furthermore, the ability to interpret the location of the iPSC colonies based on the image level label without using a pixel-wise ground truth image is more appealing and cost-effective in colony condition recognition. | |||||
書誌情報 |
Intelligent Systems with Applications 巻 17, p. 200187, 発行日 2023-01-28 |
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出版者 | ||||||
出版者 | Elsevier B.V. | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 26673053 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1016/j.iswa.2023.200187 | |||||
権利 | ||||||
権利情報 | ⓒ 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
引用 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Intelligent Systems with Applications, 17, art. no. 200187; 2023 |