ReviewBioengineering (Basel, Switzerland)2023
Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis.
Review in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
15 citing papers in PubMed.
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- A novel multimodal framework integrating pathomics, deep learning, and machine learning for breast cancer histological grades classification.Diagnostic pathology · 2026Article
- A multi-task and explainable swin transformer framework for cross-scale computational pathology in gastrointestinal cancer.Frontiers in oncology · 2026Article
- HiGATE: hierarchical graph attention for multi-scale tissue encoder in computational pathology.Frontiers in oncology · 2026Article
- Immunoprofiling at an Institutional Scale Reveals That High Numbers of Intratumoral CD8JCO precision oncology · 2025Article
- Article
- Unlocking Deeper Insights into Medical Images with Machine Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Recent Advances in Artificial Intelligence to Improve Immunotherapy and the Use of Digital Twins to Identify Prognosis of Patients with Solid Tumors.International journal of molecular sciences · 2024Review
- Reproducibility and explainability in digital pathology: The need to make black-box artificial intelligence systems more transparent.Journal of public health research · 2024Article
- Deep Learning-driven Automatic Nuclei Segmentation of Label-free Live Cell Chromatin-sensitive Partial Wave Spectroscopic Microscopy Imaging.bioRxiv : the preprint server for biology · 2024Article
- An Unsupervised Learning Tool for Plaque Tissue Characterization in Histopathological Images.Sensors (Basel, Switzerland) · 2024Article
- Deep Transfer Learning Using Real-World Image Features for Medical Image Classification, with a Case Study on Pneumonia X-ray Images.Bioengineering (Basel, Switzerland) · 2024Article
- Enhanced Nuclei Segmentation and Classification via Category Descriptors in the SAM Model.Bioengineering (Basel, Switzerland) · 2024Article
- Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits.Toxics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
This review furnishes an exhaustive analysis of the latest advancements in deep learning techniques applied to whole slide images (WSIs) in the context of cancer prognosis, focusing specifically on publications from 2019 through 2023. The swiftly maturing field of deep learning, in combination with the burgeoning availability of WSIs, manifests significant potential in revolutionizing the predictive modeling of cancer prognosis. In light of the swift evolution and profound complexity of the field, it is essential to systematically review contemporary methodologies and critically appraise their ramifications. This review elucidates the prevailing landscape of this intersection, cataloging major developments, evaluating their strengths and weaknesses, and providing discerning insights into prospective directions. In this paper, a comprehensive overview of the field aims to be presented, which can serve as a critical resource for researchers and clinicians, ultimately enhancing the quality of cancer care outcomes. This review's findings accentuate the need for ongoing scrutiny of recent studies in this rapidly progressing field to discern patterns, understand breakthroughs, and navigate future research trajectories.
Indexed as
Identifiers
What Socratic 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 Socratic graph.