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收稿日期：2023-09-19，
修回日期：2024-01-16，
纸质出版日期：2024-11-16
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目的
2
病理切片扫描仪成像的数字病理图像的聚焦质量不佳，会严重影响肿瘤诊断的准确性。因此，开展对数字病理图像的聚焦质量评估的自动化算法至关重要。现有的聚焦质量评估主要采用深度学习方法，但常规的卷积神经网络（convolutional neural network， CNN）存在全局信息提取能力差和计算量过大问题。为此，提出一种感受野扩增的轻量级病理图像聚焦质量评估网络。
方法
2
该网络引入大卷积核来扩增网络的感受野，以捕获更多的全局信息。再利用新的双流大核注意力机制，增强对空间和通道上全局信息的提取能力。最后，将该网络优化为参数量递减的大型、中型和小型3个版本，以实现网络的轻量化。
结果
2
本文提出的大型网络比同类先进方法取得更优的性能。与本文的大型网络相比，优化后的小型网络牺牲了较小的性能，却取得参数量、计算量和CPU推理时间的显著下降。与同类轻量级网络SDCNN（self-defined convolutional neural network）相比，本文的小型网络在SRCC（Spearman’s rank correlation coefficient）、PLCC（Pearson linear correlation coefficient）和KRCC（Kendall rank correlation coefficient）等度量指标上分别提升了0.016 1、0.016 6和0.029 9，而参数量、计算量和CPU推理时间分别减少了39.06%、95.11%和51.91%。
结论
2
本文提出的方法可有效地提取数字病理图像的全局聚焦信息，且计算资源消耗更低，具有现实可行性。
Objective
2
Histopathology is the gold standard for tumor diagnosis. With the development of digital pathology slide scanners， digital pathology has introduced revolutionary changes to clinical pathological diagnosis. Pathologists use digital images to examine tissues and make diagnoses based on the characteristics of the observed tissues. Simultaneously， these digital images are fed into a computer-aided diagnostic system for automated diagnosis， thereby speeding up diagnosis. However， the quality of digital pathology images is blurred locally or globally by the focusing errors produced in the scanning process. For pathologists， these blurred areas will prevent accurate observations of tissue and cellular structures， leading to misdiagnosis. Therefore， studying the focus quality evaluation for pathological images is crucial. Methods based on machine and deep learning are currently available for this research. In machine learning-based methods， features are artificially designed with the help of a priori knowledge， such as optical or microscopic imaging， and fed into a classifier to automatically obtain focused predictions. However， these methods do not automatically learn the focus features in pathological images， resulting in low evaluation accuracy. Meanwhile， deep learning-based methods automatically learn complex features， substantially improving evaluation accuracy. Current learning-based work enhances the capability to process global focus information from pathological images by introducing attention mechanisms. However， the receptive scope of these attention mechanisms is limited， which results in inadequate global focus information. By contrast， the existing networks with better performance require a larger number of parameters and computations， increasing the difficulty of their application in practice. In this paper， a focus quality assessment network with amplified receptive field （ARF-FQANet） is proposed to address challenges such as poor global information extraction and excessive computations.
Method
2
In ARF-FQANet， a large convolution kernel is used to amplify the receptive field of the network， and the dual-stream large kernel attention （DsLKA） mechanism is then integrated. In DsLKA， large kernel channel and spatial attentions are proposed to capture the global focus information in channels and spaces， respectively. The proposed large kernel channel attention is better than the classical channel attention mechanism， and the introduced large kernel retransmit squeeze （LKRS） method redistributes the weights in the space， thus avoiding the problem of losing saliency weights in classical channel attention. However， the local cellular semantic information gradually becomes salient with the downsampling of input features， which may affect the capability of the network to represent focus information. A local stable downsampling block （LSDSB） is designed to address the above problems. Extraneous information is minimized during the upsampling and downsampling processes by integrating LSDSB， thus ensuring the local stability of the features. A short branch is introduced to create a residual attention block （RAB） based on DsLKAB and LSDSB modules. In this short branch， the noise is extracted using a minimum pooling operation， which effectively suppresses the learning of noisy information during backpropagation， thus improving the capability of the network to represent focus information. In addition， an initial feature enhancement block （IFEB） is introduced at the initial stage of the network to enhance the capability of the initial layer to represent the focus information. The features obtained by IFEB provide highly comprehensive information for subsequent networks. A strategy to decompose large convolutional kernels is introduced to obtain a lightweight network， which substantially reduces the number of parameters and computational requirements. By contrast， the network parameters are reduced to achieve further compression. The network is then optimized into three aspects： large， medium， and small， each with a reduced number of parameters.
Result
2
Comparative experiments are performed on a publicly available dataset of focused quality assessment of pathology images. The compared networks are categorized as small， medium， and large according to the number of their parameters. In terms of large networks， the proposed large network performs the best with 0.765 8， 0.957 8， 0.956 2， and 0.852 3 for RMSE， SRCC， PLCC， and KRCC， respectively. These results show that the predicted focus scores are highly consistent with the actual focus scores. In terms of small and medium networks， the performance of the proposed small and medium networks is slightly degraded， but its parameters and computational complexity are notably reduced. Compared with self-defined convolutional neural network （SDCNN）， the parameters of the small network （ARF-FQANet-S）， the floating-point operations， and the CPU reference time （CPU-Time） are reduced by 39.06%， 95.11%， and 51.91%， respectively. The small network may not be able to outperform the FocusLiteNN network in terms of speed； however， performance comparable to larger networks is still provided. This paper visualizes the receptive field of several networks in different stages. The results indicate that the ARF-FQANet proposed in this paper obtains larger receptive fields， especially in the initial layer of the network. Thus， additional global focusing information is obtained at the initial layer of the network， which contributes to the stable performance of the small ARF-FQANet.
Conclusion
2
Compared with similar methods， the proposed network efficiently extracts global focus information from pathological images. In this network， a large convolutional kernel is used to expand the receptive field of the network， and DsLKA is introduced to enhance the global information within the learning space and channels. This strategy ensures that the network maintains competitive performance even after notable parameter reductions. The small network （ARF-FQANet-S） offers remarkable advantages in terms of CPU inference time and is ideal for lightweight deployments on edge devices. Overall， the results provide a technical reference for the lightweight models.
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