ArticleScientific reports2023
Region of interest (ROI) selection using vision transformer for automatic analysis using whole slide images.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Multi-window scanning method for region-of-interest selection in pancreatic endoscopic ultrasound images.Journal of ultrasound · 2026Article
- NicheProt: Cell-type-resolved proteomics of tissue compartments.Science advances · 2026Article
- Longitudinal evaluation of tumor-infiltrating lymphocyte scoring using automated region of interest registration in breast cancer.Breast cancer research : BCR · 2026Article
- Predicting Response to Immune Checkpoint Inhibitors in Melanoma: Emerging Approaches in Digital Pathology, Spatial Profiling and Machine Learning.International journal of molecular sciences · 2026Review
- A comparative analysis of deep learning models for disease classification in multi-organ histopathological images.Scientific reports · 2026Article
- Swarm intelligence-guided ROI selection for deep learning assessment of HER2 in colorectal cancer.Scientific reports · 2026Article
- Cracking the Code: Computational Image Analysis Tools for Histopathological and Morphometric Insights.Journal of imaging · 2026Review
- Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings.Journal of pathology informatics · 2026Article
- Imaging-Based Prediction of Key Breast Cancer Biomarkers Using Deep Learning on Digital Breast Tomosynthesis.European journal of breast health · 2026Article
- Automated Kellgren-Lawrence grading of knee osteoarthritis using a multi-scale attention-based deep learning framework.Frontiers in medicine · 2026Article
- Review
- HSSAM-Net: hyper-scale shifted aggregation network for precise colorectal polyp segmentation in endoscopic images.Scientific reports · 2025Article
- Article
- Detection and classification of supraspinatus pathologies on shoulder magnetic resonance images using a code-free deep learning application.Asia-Pacific journal of sports medicine, arthroscopy, rehabilitation and technology · 2025Article
- Evaluation of Fluorescence Detection Algorithms for Efficient ROI Setting in Low-Cost Real-Time PCR Systems.Biosensors · 2025Article
- Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.Journal of imaging informatics in medicine · 2025Article
- AI-Assisted Cell Culture System.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Impact of signal-to-noise ratio and contrast definition on the sensitivity assessment and benchmarking of fluorescence molecular imaging systems.Journal of biomedical optics · 2025Article
- Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry.Brain sciences · 2024Article
- Article
Corrections and comments
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Authors and funding
7 authors.
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Abstract
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.
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