A B S T R A C TBreast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time-and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology.

Background Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Results Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

Purpose: From the perspective of positive psychology, our study aimed to explore depressive symptoms and psychological well-being among Chinese nurses, as well as analyze the impacts of character strengths, self-efficacy and social support on the mental health of nurses. Methods: A cross-sectional and descriptive design using five self-reported questionnaires was used to investigate a cohort of 4238 nurses during 2018. A structural equation modeling analysis was used to verify a hypothetical model linking character strengths, self-efficacy, social support, depressive symptoms, and psychological well-being. Results: The prevalence of depression among this cohort of Chinese nurses was 58.1%. The mean scores for caring, inquisitiveness, and self-control were 19.93 (SD ¼ 2.82), 15.94 (SD ¼ 3.00), and 16.34 (SD ¼ 2.95), respectively. The hypothesized model was a good fit of the data (c 2 /df ¼ 1.77, p ¼ .183, root mean square error of approximation ¼ 0.04, goodness of fit index ¼ 1.00, comparative fit index ¼ 1.00, TuckereLewis index ¼ 1.00). Except for the path from self-control to depression, the other hypothetical paths investigated were statistically significant. Conclusion: Character strengths were directly and positively associated with psychological well-being. Inquisitiveness was the strongest direct protective factor for depression. In addition, character strengths indirectly alleviated depression and increased psychological well-being through mediating variables of social support and self-efficacy. This study should alert nurse managers that more attention should be paid to the character strengths and mental health of nurses. This study provides evidence for interventions based on character strengths as a management strategy to support the mental health of nurses.

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