Breast cancer in different molecular subtypes, which is determined by the overexpression rates of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), progesterone receptor (PR), and Ki67, exhibit distinct symptom characteristics and sensitivity to different treatment. The immunohistochemical method, one of the most common detecting tools for tumour markers, is heavily relied on artificial judgment and in clinical practice, with an inherent limitation in interpreting stability and operating efficiency. Here, a holistic intelligent breast tumour diagnosis system has been developed for tumour-markeromic analysis, combining the automatic interpretation and clinical suggestion. The holistic intelligent breast tumour diagnosis system included two main modules. The interpreting modules were constructed based on convolutional neural network, for comprehensively extracting and analyzing the multi-features of immunostaining. Referring to the clinical classification criteria, the interpreting results were encoded in a low-dimensional feature representation in the subtyping module, to efficiently output a holistic detecting result of the critical tumour-markeromic with diagnosis suggestions on molecular subtypes. The overexpression rates of HER2, ER, PR, and Ki67, as well as an effective determination of molecular subtypes were successfully obtained by this diagnosis system, with an average sensitivity of 97.6 % and an average specificity of 96.1 %, among those, the sensitivity and specificity for interpreting HER2 were up to 99.8 % and 96.9 %. The holistic intelligent breast tumour diagnosis system shows improved performance in the interpretation of immunohistochemical images over pathologist-level, which can be expected to overcome the limitations of conventional manual interpretation in efficiency, precision, and repeatability.

Nowadays, more and more people believe blockchains enable transform our economies and societies fundamentally. Scientists discuss blockchain and its application from a technical perspective. This study explores the trends of blockchain research in the management and business domain by use of bibliometric and content analysis. Two hundred eighty-two articles retrieved from SSCI show the study in the area started in 2017 but grew exponentially. The most productive country is the US, China, and the UK. Four main research themes are identified, i.e., impact on business and innovation, impact on the financial industry, impact on operations and supply chains, and technological management. We found researcher's concern has shifted from conceptual explanations and descriptions at a macro-level to highlight specific implementation. Also, we found that the largest body of blockchain research is about the supply chains instead of the financial industry in the management and business domain.