Department of Electronics and Communication Engineering, National Institute Technology Karnataka, Surathkal, Mangaluru, Karnataka, 575025, India.
Department of pathology, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Int J Comput Assist Radiol Surg. 2021 Sep;16(9):1549-1563. doi: 10.1007/s11548-021-02410-4. Epub 2021 May 30.
Liver cancer is one of the most common types of cancers in Asia with a high mortality rate. A common method for liver cancer diagnosis is the manual examination of histopathology images. Due to its laborious nature, we focus on alternate deep learning methods for automatic diagnosis, providing significant advantages over manual methods. In this paper, we propose a novel deep learning framework to perform multi-class cancer classification of liver hepatocellular carcinoma (HCC) tumor histopathology images which shows improvements in inference speed and classification quality over other competitive methods.
The BreastNet architecture proposed by Togacar et al. shows great promise in using convolutional block attention modules (CBAM) for effective cancer classification in H&E stained breast histopathology images. As part of our experiments with this framework, we have studied the addition of atrous spatial pyramid pooling (ASPP) blocks to effectively capture multi-scale features in H&E stained liver histopathology data. We classify liver histopathology data into four classes, namely the non-cancerous class, low sub-type liver HCC tumor, medium sub-type liver HCC tumor, and high sub-type liver HCC tumor. To prove the robustness and efficacy of our models, we have shown results for two liver histopathology datasets-a novel KMC dataset and the TCGA dataset.
Our proposed architecture outperforms state-of-the-art architectures for multi-class cancer classification of HCC histopathology images, not just in terms of quality of classification, but also in computational efficiency on the novel proposed KMC liver data and the publicly available TCGA-LIHC dataset. We have considered precision, recall, F1-score, intersection over union (IoU), accuracy, number of parameters, and FLOPs as metrics for comparison. The results of our meticulous experiments have shown improved classification performance along with added efficiency. LiverNet has been observed to outperform all other frameworks in all metrics under comparison with an approximate improvement of [Formula: see text] in accuracy and F1-score on the KMC and TCGA-LIHC datasets.
To the best of our knowledge, our work is among the first to provide concrete proof and demonstrate results for a successful deep learning architecture to handle multi-class HCC histopathology image classification among various sub-types of liver HCC tumor. Our method shows a high accuracy of [Formula: see text] on the proposed KMC liver dataset requiring only 0.5739 million parameters and 1.1934 million floating point operations per second.
肝癌是亚洲最常见的癌症类型之一，死亡率很高。肝癌的一种常见诊断方法是手动检查组织病理学图像。由于其繁琐的性质，我们专注于替代深度学习方法进行自动诊断，这为手动方法提供了显著的优势。在本文中，我们提出了一种新的深度学习框架，用于对肝细胞癌（HCC）肿瘤组织病理学图像进行多类癌症分类，该框架在推理速度和分类质量方面优于其他竞争方法。
Togacar 等人提出的 BreastNet 架构在使用卷积块注意力模块（CBAM）对 H&E 染色的乳腺癌组织病理学图像进行有效癌症分类方面显示出巨大的潜力。作为我们对该框架进行的实验的一部分，我们研究了在 H&E 染色的肝组织病理学数据中添加空洞空间金字塔池化（ASPP）块以有效捕获多尺度特征的情况。我们将肝组织病理学数据分为四类，即非癌性、低亚型肝 HCC 肿瘤、中亚型肝 HCC 肿瘤和高亚型肝 HCC 肿瘤。为了证明我们模型的稳健性和有效性，我们展示了两个肝组织病理学数据集（一个新的 KMC 数据集和 TCGA 数据集）的结果。
我们提出的架构在 HCC 组织病理学图像的多类癌症分类方面优于最先进的架构，不仅在分类质量方面，而且在新提出的 KMC 肝脏数据和公开的 TCGA-LIHC 数据集上的计算效率方面也是如此。我们考虑了精度、召回率、F1 分数、交并比（IoU）、准确性、参数数量和 FLOPs 作为比较的指标。我们精心进行的实验结果表明，在 KMC 和 TCGA-LIHC 数据集上，分类性能得到了提高，同时效率也得到了提高。与所有其他框架相比，LiverNet 在所有比较指标下都表现出了更好的性能，在 KMC 和 TCGA-LIHC 数据集上的准确性和 F1 分数提高了约[Formula: see text]。
据我们所知，我们的工作是首批提供具体证据并证明深度学习架构成功处理各种肝 HCC 肿瘤亚型的多类 HCC 组织病理学图像分类的工作之一。我们的方法在新提出的 KMC 肝脏数据集上的准确率达到了[Formula: see text]，仅需要 0.5739 万个参数和 1.1934 万个浮点运算每秒。
