篇名 |
Deep Learning for Joint Classification and Segmentation of Histopathology Image
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並列篇名 | Deep Learning for Joint Classification and Segmentation of Histopathology Image |
作者 | Hyun-Cheol Park、Raman Ghimire、Sahadev Poudel、Sang-Woong Lee |
英文摘要 | Liver cancer is one of the most prevalent cancer deaths worldwide. Thus, early detection and diagnosis of possible liver cancer help in reducing cancer death. Histopathological Image Analysis (HIA) used to be carried out traditionally, but these are time-consuming and require expert knowledge. We propose a patch-based deep learning method for liver cell classification and segmentation. In this work, a two-step approach for the classification and segmentation of whole-slide image (WSI) is proposed. Since WSIs are too large to be fed into convolutional neural networks (CNN) directly, we first extract patches from them. The patches are fed into a modified version of U-Net with its equivalent mask for precise segmentation. In classification tasks, the WSIs are scaled 4 times, 16 times, and 64 times respectively. Patches extracted from each scale are then fed into the convolutional network with its corresponding label. During inference, we perform majority voting on the result obtained from the convolutional network. The proposed method has demonstrated better results in both classification and segmentation of liver cancer cells.
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起訖頁 | 903-910 |
關鍵詞 | Histopathological image analysis、Whole-slide image、Segmentation、Classification、Patch-based method |
刊名 | 網際網路技術學刊 |
期數 | 202207 (23:4期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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