PathNarratives: Data annotation for pathological human-AI collaborative diagnosis

Pathology is the gold standard of clinical diagnosis. Artificial intelligence (AI) in pathology becomes a new trend, but it is still not widely used due to the lack of necessary explanations for pathologists to understand the rationale. Clinic-compliant explanations besides the diagnostic decision of pathological images are essential for AI model training to provide diagnostic suggestions assisting pathologists practice. In this study, we propose a new annotation form, PathNarratives, that includes a hierarchical decision-to-reason data structure, a narrative annotation process, and a multimodal interactive annotation tool. Following PathNarratives, we recruited 8 pathologist annotators to build a colorectal pathological dataset, CR-PathNarratives, containing 174 whole-slide images (WSIs). We further experiment on the dataset with classification and captioning tasks to explore the clinical scenarios of human-AI-collaborative pathological diagnosis. The classification tasks show that fine-grain prediction enhances the overall classification accuracy from 79.56 to 85.26%. In Human-AI collaboration experience, the trust and confidence scores from 8 pathologists raised from 3.88 to 4.63 with providing more details. Results show that the classification and captioning tasks achieve better results with reason labels, provide explainable clues for doctors to understand and make the final decision and thus can support a better experience of human-AI collaboration in pathological diagnosis. In the future, we plan to optimize the tools for the annotation process, and expand the datasets with more WSIs and covering more pathological domains

Molecular Pathology in the Lungs of Severe Acute Respiratory Syndrome Patients

Severe acute respiratory syndrome (SARS) is a novel infectious disease with disastrous clinical consequences, in which the lungs are the major target organs. Previous studies have described the general pathology in the lungs of SARS patients and have identified some of the cell types infected by SARS coronavirus (SARS-CoV). However, at the time of this writing, there were no comprehensive reports of the cellular distribution of the virus in lung tissue. In this study, we have performed double labeling combining in situ hybridization with immunohistochemistry and alternating each of these techniques separately in consecutive sections to evaluate the viral distribution on various cell types in the lungs of seven patients affected with SARS. We found that SARS-CoV was present in bronchial epithelium, type I and II pneumocytes, T lymphocytes, and macrophages/monocytes. For pneumocytes, T lymphocytes, and macrophages, the infection rates were calculated. In addition, our present study is the first to demonstrate infection of endothelial cells and fibroblasts in SARS