Based on the Nottingham Histopathology Grading (NHG) system, mitosis cells detection is one of the important criteria to determine the grade of breast carcinoma. Mitosis cells detection is a challenging task due to the heterogeneous microenvironment of breast histopathology images. Recognition of complex and inconsistent objects in the medical images could be achieved by incorporating domain knowledge in the field of interest. In this study, the strategies of the histopathologist and domain knowledge approach were used to guide the development of the image processing framework for automated mitosis cells detection in breast histopathology images. The detection framework starts with color normalization and hyperchromatic nucleus segmentation. Then, a knowledge-assisted false positive reduction method is proposed to eliminate the false positive (i.e., non-mitosis cells). This stage aims to minimize the percentage of false positive and thus increase the F1-score. Next, features extraction was performed. The mitosis candidates were classified using a Support Vector Machine (SVM) classifier. For evaluation purposes, the knowledge-assisted detection framework was tested using two datasets: a custom dataset and a publicly available dataset (i.e., MITOS dataset). The proposed knowledge-assisted false positive reduction method was found promising by eliminating at least 87.1% of false positive in both the dataset producing promising results in the F1-score. Experimental results demonstrate that the knowledge-assisted detection framework can achieve promising results in F1-score (custom dataset: 89.1%; MITOS dataset: 88.9%) and outperforms the recent works.
Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.
The prevalence of vaping worldwide is showing an upward trend. This study aimed to determine the factors associated with motivation to quit vaping among vapers in the Federal Territory of Kuala Lumpur, Malaysia, through a cross-sectional, purposive sampling study. Respondents were required to complete a questionnaire consisting of vapers' sociodemographic questions, habitual behavioral pattern questions, the e-Fagerström Test of Nicotine Dependence, the Glover-Nilsson Smoking Behavioral Dependence Questionnaire, perception questions, motivation to quit questions, and withdrawal symptom questions. A total of 311 vapers participated in this study. The majority of the vapers were male (84.6%), younger (18-25 years) (55.3%), and with monthly income less than RM 4000 (USD 868; 83.9%). The level of motivation to quit vaping was found to have a significant association with the perception of vaping being as satisfying as cigarette smoking (p = 0.006) and mild to very strong nicotine dependence (p = 0.001). Participants who recorded moderate and strong habitual vaping behaviors had lower odds of having high motivation to quit vaping compared to those recording slight habitual behaviors (OR = 0.279, 95%CI(0.110-0.708), p = 0.007 and OR = 0.185, 95%CI(0.052-0.654), p = 0.009, respectively). Factors associated with higher motivation to quit vaping could be explored to gain better understanding of how to increase their motivation level for future quit attempts.