###### Abstract  
Prostate cancer (PCa) is one of the most prevalent cancers worldwide. As the demand for prostate biopsies increases, a worldwide shortage and an uneven geographical distribution of proficient pathologists place a strain on the efficacy of pathological diagnosis. Deep learning (DL) is able to automatically extract features from whole-slide images of prostate biopsies annotated by skilled pathologists and to classify the severity of PCa. A whole-slide image of biopsies has many irrelevant features that weaken the performance of DL models. To enable DL models to focus more on cancerous tissues, we propose a Multi-Channel and Multi-Spatial (MCMS) Attention module that can be easily plugged into any backbone CNN to enhance feature extraction. Specifically, MCMS learns a channel attention vector to assign weights to channels in the feature map by pooling from multiple attention branches with different reduction ratios; similarly, it also learns a spatial attention matrix to focus on more relevant areas of the image, by pooling from multiple convolutional layers with different kernel sizes. The model is verified on the most extensive multi-center PCa dataset that consists of 11,000 H&E-stained histopathology whole-slide images. Experimental results demonstrate that an MCMS-assisted CNN can effectively boost prediction performance in accuracy (ACC) and quadratic weighted kappa (QWK), compared with prior studies. The proposed model and results can serve as a credible benchmark for future research in automated PCa grading. prostate cancer; ISUP grading; channel attention; spatial attention; convolutional neural network; reduction ratio; kernel size +
Footnote †: journal: Medical  
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Pediatrics (ABP) certification to be eligible to issue a pathology report [4]. Additionally, the working conditions of pathologists and equipment in grassroots and county-level hospitals are poor, which cannot attract high-level pathologists. The pathology departments in many countries are short-staffed. In some less affluent African countries, there is only one pathologist per million population [5]. The lack of a workforce may cause pathological diagnosis to be performed by under-trained people like clinical laboratory staff, increasing the chance of misdiagnosis and medical disputes. To improve the efficiency of PCa diagnosis, recent advances have explored various machine learning-based PCa grading techniques [6; 7; 8; 9]. An accurate and robust PCa grading algorithm needs to address several challenges, including the insufficiency of the fine-grained labels, the morphological heterogeneity of slide contents, high resolutions with large area blank, and artifacts caused by stain variations [6]. With the progress made in hardware, datasets, and algorithms, deep learning (DL) has played an increasingly crucial role in PCa detection and classification. Compared to feature-based models [10; 11], DL models can automatically extract feature information from histopathology whole-slide images and grade the severity of PCa, without the costly step of feature engineering [7]. Exploiting the rich and original information indicative of cancerous epithelium from a whole-slide image is the core mission of DL-based PCa grading techniques. There has been a recent trend that PCa datasets have evolved from mono-center to extensive multi-center datasets [8; 12; 13], and annotations have gradually shifted from regions of interest (RoIs) to the whole-slide-level labels. Studies that focus on classifying pre-defined small RoIs hand-picked by pathologists [14; 15; 16] are limited since the models are not able to gain global knowledge in the slide level. One of the proposed solutions is Multiple Instance Learning, where a slide is represented as a bag and tiles within the bag are treated as instances [17; 18]. Additionally, attention mechanisms have been leveraged to enhance the feature representation of deep models. [PERSON] et al.