ReviewFrontiers in artificial intelligence2021
Deep Learning of Histopathology Images at the Single Cell Level.
Review in Frontiers in artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 57 citations in OpenAlex.
- Nationwide federated learning for histopathology: secure deployment across Germany behind firewalls.NPJ digital medicine · 2026Article
- A deep learning framework for histopathological analysis of pixel-level extracellular matrix variation in standard H&E-stained images.Scientific reports · 2026Article
- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Technical note: Impact of tissue section thickness on accuracy of cell classification with a deep learning network.Journal of pathology informatics · 2025Article
- Cell type prediction with neighborhood-enhanced cellular embedding using deep learning on hematoxylin and eosin-stained images.Computational and structural biotechnology journal · 2025Article
- Leveraging deep learning for identification and segmentation of "CAF-1/p60-positive" nuclei in oral squamous cell carcinoma tissue samples.Journal of pathology informatics · 2024Article
- Artificial Intelligence in Histopathology.Journal of pharmacy & bioallied sciences · 2024Review
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Scalable deep learning artificial intelligence histopathology slide analysis and validation.Scientific reports · 2024Article
- Histopathological Alterations inInsects · 2024Article
- Article
- Automated bone marrow cell classification through dual attention gates dense neural networks.Journal of cancer research and clinical oncology · 2023Article
- SIC50: Determining drug inhibitory concentrations using a vision transformer and an optimized Sobel operator.Patterns (New York, N.Y.) · 2023Article
- NuKit: A deep learning platform for fast nucleus segmentation of histopathological images.Journal of bioinformatics and computational biology · 2023Article
- Application of Artificial Intelligence in Pathology: Trends and Challenges.Diagnostics (Basel, Switzerland) · 2022Review
- Synthesis of Microscopic Cell Images Obtained from Bone Marrow Aspirate Smears through Generative Adversarial Networks.Biology · 2022Article
- Computational solutions for spatial transcriptomics.Computational and structural biotechnology journal · 2022Review
- Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer.Frontiers in immunology · 2022Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
The tumor immune microenvironment (TIME) encompasses many heterogeneous cell types that engage in extensive crosstalk among the cancer, immune, and stromal components. The spatial organization of these different cell types in TIME could be used as biomarkers for predicting drug responses, prognosis and metastasis. Recently, deep learning approaches have been widely used for digital histopathology images for cancer diagnoses and prognoses. Furthermore, some recent approaches have attempted to integrate spatial and molecular omics data to better characterize the TIME. In this review we focus on machine learning-based digital histopathology image analysis methods for characterizing tumor ecosystem. In this review, we will consider three different scales of histopathological analyses that machine learning can operate within: whole slide image (WSI)-level, region of interest (ROI)-level, and cell-level. We will systematically review the various machine learning methods in these three scales with a focus on cell-level analysis. We will provide a perspective of workflow on generating cell-level training data sets using immunohistochemistry markers to "weakly-label" the cell types. We will describe some common steps in the workflow of preparing the data, as well as some limitations of this approach. Finally, we will discuss future opportunities of integrating molecular omics data with digital histopathology images for characterizing tumor ecosystem.
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What OpenQuestion holds
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.