Deep transfer learning based model for colorectal cancer histopathology segmentation: A comparative study of deep pre-trained models
Deep transfer learning based model for colorectal cancer histopathology segmentation: A comparative study of deep pre-trained models
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DOI：
10.1016/j.ijmedinf.2021.104669
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发表时间：
2022-03-01
影响因子：
4.9
通讯作者：
Deters, Ralph
中科院分区：
文献类型：
--
作者：
Kassani, Sara Hosseinzadeh;Kassani, Peyman Hosseinzadeh;Deters, Ralph
结直肠癌是世界范围内癌症相关死亡的主要原因之一。早期发现可疑组织可显著提高存活率。在这项研究中，评估了各种基于深度学习的架构的性能，用于结直肠组织样本的自动肿瘤分割。所提出的方法突出了在组织病理学图像分析的分割架构的编码器部分中结合卷积神经网络模块和转移学习的效用。对一个具有挑战性的组织病理学分割任务进行了比较和广泛的实验，以证明在分割编码器-解码器网络中引入深度模块的有效性及其组件的贡献。实验结果表明，共享DenseNet和LinkNet架构是有前途的，达到了最先进的性能，并优于其他方法，骰子相似性指数为82.74%+/- 1.77，准确率为87.07%+/- 1.56，f1-评分值为82.79%+/- 1.79。
Colorectal cancer is one of the leading causes of cancer-related death, worldwide. Early detection of suspicious tissues can significantly improve the survival rate. In this study, the performance of a wide variety of deep learning-based architectures is evaluated for automatic tumor segmentation of colorectal tissue samples. The proposed approach highlights the utility of incorporating convolutional neural network modules and transfer learning in the encoder part of a segmentation architecture for histopathology image analysis. A comparative and extensive experiment was conducted on a challenging histopathological segmentation task to demonstrate the effectiveness of incorporating deep modules in the segmentation encoder-decoder network as well as the contributions of its components. Experimental results demonstrate that shared DenseNet and LinkNet architecture is promising, achieves the state-of-the-art performance, and outperforms other methods with a dice similarity index of 82.74%+/- 1.77, accuracy of 87.07%+/- 1.56, and f1-score value of 82.79%+/- 1.79.