A Preliminary Examination for a Study on Specialized Training : The Examination of the Studies on Profession in the English-Speaking World

The purpose of this paper is to examine studies on profession. It is the preliminary examination for a study on specialized training in Japan. Mainly, the following two points become clear. First, there are problems such as cultural bias and lack of historical viewpoints in the studies on profession in the English-speaking world. Second, recent studies on profession use the concepts of \u27profession\u27and so on effectively, but those concepts have some problems. In conclusion, I argue on the importance of the study taking account of the mentioned points and Japanese special characteristics

A Historical Study on the Relation between Vocational Qualification System and Specialized Training Colleges

The purpose of this paper is to clarify the relation between vocational qualification system and specialized training colleges. Through a historical investigation, the following two points become clear about the relation. First, it is cleared that, since vocational qualification system has not had a definite idea and regular schools have been in short supply, specialized training colleges have been admitted as training schools for vocational qualifications. Second, it is cleared that, only specialized training colleges admitted as training schools for vocational qualifications have developed in their own way. In conclusion, specialized training colleges should be classified according to the vocational qualification, and vocational qualification system should have a definite idea in the future

Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient