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.
Since the outbreak of the COVID-19 epidemic, a large number of disposable protective masks have been manufactured and used, and the abandonment of masks has caused enormous pollution. In this paper the chitosan (CS), polyvinyl alcohol (PVA), and water were used as raw materials and the nanofiber membranes were prepared by electrostatic spinning. The CS/PVA fiber membranes were crosslinked by glutaraldehyde hydrochloric acid vapour. The fiber morphology, hydrolysis resistance, antibacterial properties, chemical structure, thermal stability and filtration performance of nanofiber membranes were characterized. Results shows that the antibacterial performance of the crosslinked composite nanofibers exceeds 97%, the thermal stability is improved, and the fiber morphology is not destroyed. The hybridized fiber membrane has high filtration performance, excellent antibacterial and hydrolysis resistance, which broadening the PVA fiber membrane application. It is expected to replace traditional protective materials and relieve environmental pressure.
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