Region of interest (ROI) selection using vision transformer for automatic analysis using whole slide images.

Selecting regions of interest (ROI) is a common step in medical image analysis across all imaging modalities. An ROI is a subset of an image appropriate for the intended analysis and identified manually by experts. In modern pathology, the analysis involves processing multidimensional and high resolution whole slide image (WSI) tiles automatically with an overwhelming quantity of structural and functional information. Despite recent improvements in computing capacity, analyzing such a plethora of data is challenging but vital to accurate analysis. Automatic ROI detection can significantly reduce the number of pixels to be processed, speed the analysis, improve accuracy and reduce dependency on pathologists. In this paper, we present an ROI detection method for WSI and demonstrated it for human epidermal growth factor receptor 2 (HER2) grading for breast cancer patients. Existing HER2 grading relies on manual ROI selection, which is tedious, time-consuming and suffers from inter-observer and intra-observer variability. This study found that the HER2 grade changes with ROI selection. We proposed an ROI detection method using Vision Transformer and investigated the role of image magnification for ROI detection. This method yielded an accuracy of 99% using 20 × WSI and 97% using 10 × WSI for the ROI detection. In the demonstration, the proposed method increased the diagnostic agreement to 99.3% with the clinical scores and reduced the time to 15 seconds for automated HER2 grading.

Singular Nuclei Segmentation for Automatic HER2 Quantification Using CISH Whole Slide Images.

Human epidermal growth factor receptor 2 (HER2) quantification is performed routinely for all breast cancer patients to determine their suitability for HER2-targeted therapy. Fluorescence in situ hybridization (FISH) and chromogenic in situ hybridization (CISH) are the US Food and Drug Administration (FDA) approved tests for HER2 quantification in which at least 20 cancer-affected singular nuclei are quantified for HER2 grading. CISH is more advantageous than FISH for cost, time and practical usability. In clinical practice, nuclei suitable for HER2 quantification are selected manually by pathologists which is time-consuming and laborious. Previously, a method was proposed for automatic HER2 quantification using a support vector machine (SVM) to detect suitable singular nuclei from CISH slides. However, the SVM-based method occasionally failed to detect singular nuclei resulting in inaccurate results. Therefore, it is necessary to develop a robust nuclei detection method for reliable automatic HER2 quantification. In this paper, we propose a robust U-net-based singular nuclei detection method with complementary color correction and deconvolution adapted for accurate HER2 grading using CISH whole slide images (WSIs). The efficacy of the proposed method was demonstrated for automatic HER2 quantification during a comparison with the SVM-based approach.

The Perception of Health Professionals in Bangladesh toward the Digitalization of the Health Sector.

Bangladesh is undertaking a major transformation towards digitalization in every sector, and healthcare is no exception. Digitalization of the health sector is expected to improve healthcare services while reducing human effort and ensuring the satisfaction of patients and health professionals. However, for practical and successful digitalization, it is necessary to understand the perceptions of health professionals. Therefore, we conducted a cross-sectional survey in Bangladesh to investigate health professionals' perceptions in relation to various socio-demographic variables such as age, gender, location, profession and institution. We also evaluated their competencies, as digital health-related competencies are required for digitalization. Additionally, we identified major digitalization challenges. Quantitative survey data were analyzed with Python Pandas, and qualitative data were classified using Valence-Aware Dictionary and Sentiment Reasoner (VADER). This study found significant relationships between age χ2(12,N=701)=82.02,p<0.001; location χ2(4,N=701)=18.78,p<0.001; and profession χ2(16,N=701)=71.02,p<0.001; with technical competency. These variables also have similar influences on psychological competency. According to VADER, 88.1% (583/701) of respondents have a positive outlook toward digitalization. The internal consistency of the survey was confirmed by Cronbach's alpha score (0.746). This study assisted in developing a better understanding of how professionals perceive digitalization, categorizes professionals based on competency, and prioritizes the major digitalization challenges.