ReviewJournal of pathology informatics2023
Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.
Review in Journal of pathology informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed.
- FTU-Seek: Foundation Model-Guided Hard-Negative Learning for Sparse Functional Tissue Unit Segmentation.Biomedicines · 2026Article
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- Different BI-RADS breast cancer diagnosis using MobileNetV1 and vision transformer based on explainable artificial intelligence (XAI).Scientific reports · 2026Article
- Biological feature-based machine learning in histopathological images: a systematic review.Journal of pathology informatics · 2026Review
- Multimodal Integration Enhances Tissue Image Information Content: A Deep Feature Perspective.Bioengineering (Basel, Switzerland) · 2025Article
- Advancing open-source visual analytics in digital pathology: A systematic review of tools, trends, and clinical applications.Journal of pathology informatics · 2025Review
- Article
- Enhancing basal cell carcinoma classification in preoperative biopsies via transfer learning with weakly supervised graph transformers.BMC medical imaging · 2025Article
- The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations-a narrative review.Discover oncology · 2025Review
- Advanced Deep Learning Approaches in Detection Technologies for Comprehensive Breast Cancer Assessment Based on WSIs: A Systematic Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- PathoEye: A deep learning framework for the whole-slide image analysis of skin tissue.Computational and structural biotechnology journal · 2025Article
- Lipid droplet distribution quantification method based on lipid droplet detection by constrained reinforcement learning.PloS one · 2025Article
- Integrating computed tomography and biopsy images to predict chemotherapy response in gastric cancer.Frontiers in oncology · 2025Article
- SVMVGGNet-16: A Novel Machine and Deep Learning Based Approaches for Lung Cancer Detection Using Combined SVM and VGGNet-16.Current medical imaging · 2025Article
- Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice.Frontiers in medicine · 2025Article
- HcGAN: Harmonic conditional generative adversarial network for efficiently generating high-quality IHC images from H&E.Heliyon · 2024Article
- Multi-label dental disorder diagnosis based on MobileNetV2 and swin transformer using bagging ensemble classifier.Scientific reports · 2024Article
- An Unsupervised Learning Tool for Plaque Tissue Characterization in Histopathological Images.Sensors (Basel, Switzerland) · 2024Article
- Advancements in triple-negative breast cancer sub-typing, diagnosis and treatment with assistance of artificial intelligence : a focused review.Journal of cancer research and clinical oncology · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital pathology technologies, including whole slide imaging (WSI), have significantly improved modern clinical practices by facilitating storing, viewing, processing, and sharing digital scans of tissue glass slides. Researchers have proposed various artificial intelligence (AI) solutions for digital pathology applications, such as automated image analysis, to extract diagnostic information from WSI for improving pathology productivity, accuracy, and reproducibility. Feature extraction methods play a crucial role in transforming raw image data into meaningful representations for analysis, facilitating the characterization of tissue structures, cellular properties, and pathological patterns. These features have diverse applications in several digital pathology applications, such as cancer prognosis and diagnosis. Deep learning-based feature extraction methods have emerged as a promising approach to accurately represent WSI contents and have demonstrated superior performance in histology-related tasks. In this survey, we provide a comprehensive overview of feature extraction methods, including both manual and deep learning-based techniques, for the analysis of WSIs. We review relevant literature, analyze the discriminative and geometric features of WSIs (i.e., features suited to support the diagnostic process and extracted by "engineered" methods as opposed to AI), and explore predictive modeling techniques using AI and deep learning. This survey examines the advances, challenges, and opportunities in this rapidly evolving field, emphasizing the potential for accurate diagnosis, prognosis, and decision-making in digital pathology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.