Deep Learning for Joint Classification and Segmentation of Histopathology Image,ERICDATA高等教育知識庫
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篇名
Deep Learning for Joint Classification and Segmentation of Histopathology Image
並列篇名
Deep Learning for Joint Classification and Segmentation of Histopathology Image
作者 Hyun-Cheol ParkRaman GhimireSahadev PoudelSang-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.

 

起訖頁 903-910
關鍵詞 Histopathological image analysisWhole-slide imageSegmentationClassificationPatch-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 10.53106/160792642022072304025   複製DOI
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